% This file was created with Citavi 5.7.0.0

@proceedings{248,
 title = {{19th International Conference on Pattern Recognition}}
}


@proceedings{249,
 title = {{19th International Conference on Pattern Regcognition}}
}


@book{309,
 title = {{Modular Machine Tool {\&} Automatic Manufacturing Technique}}
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@book{157,
 title = {{MongoDB Applied Design Patterns}}
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@booklet{358,
 title = {{Parallel Computing Toolbox R2015b Users Guide}}
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@book{344,
 title = {{Proc. ASCI 2002}}
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@proceedings{903,
 title = {{Proceedings of the 2018 IEEE International Conference on Industrial Technology (ICIT)}}
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@proceedings{1991,
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@proceedings{1993,
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@book{1995,
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 title = {{Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence}}
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@proceedings{1995b,
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@proceedings{1995c,
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@proceedings{1995d,
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@book{1996,
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@proceedings{1997,
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@book{1999,
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@book{1999b,
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@book{1999c,
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@proceedings{1999d,
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@book{2000,
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@book{2000b,
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@proceedings{2000c,
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 title = {{Proceedings of the Advances in Neural Information Processing Systems}}
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@proceedings{2000d,
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 title = {{Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining}}
}


@book{2000e,
 year = {2000},
 title = {{Proceedings of the Sixteenth Conference on Uncertainty in Artificial Intelligence}}
}


@proceedings{2001,
 year = {2000},
 title = {{Advances in Neural Information Processing Systems 13}}
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@proceedings{2001b,
 year = {2001},
 title = {{7th Annual Conference of the Advanced School for Computing and Imaging}}
}


@book{2001c,
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 title = {{Proceedings of the Seventeenth International Joint Confer}}
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@proceedings{2001d,
 year = {2001},
 title = {{Proceedings of the Seventeenth conference on Uncertainty in artificial intelligence}},
 publisher = {{Morgan Kaufmann Publishers Inc.}}
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@proceedings{2001e,
 year = {2001},
 title = {{Proceedings of the Seventeenth Conference on Uncertainty in Artificial Intelligence}},
 publisher = {{Morgan Kaufmann Publishers Inc.}}
}


@proceedings{2001f,
 year = {2001},
 title = {{SAE 2001 World Congress}}
}


@proceedings{2002,
 year = {2002},
 title = {{Advances in Neural Information Processsing Systems 15}}
}


@proceedings{2002b,
 year = {2002},
 title = {{Preprints of reglerm{\"o}te 2002}}
}


@book{2003,
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 title = {{2003 IEEE XIII Workshop on Neural Networks for Signal Processing--NNSP'03}}
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@book{2003b,
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 title = {{Proceedings of the Third IEEE International Conference on Data Mining}}
}


@book{2003c,
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 title = {{Proceedings of the ICANN/ICONIP 2003}}
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@proceedings{2003d,
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 title = {{Systems, Man and Cybernetics, 2003. IEEE International Conference on}}
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@proceedings{2003e,
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 title = {{Proceedings of the International Joint Conference on Neural Networks}}
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@proceedings{2004,
 year = {2004},
 title = {{Proceedings of the 17th International Conference on Pattern Recognition}}
}


@proceedings{2005,
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 title = {{Proceedings of the International Conference on Communications in Computing}}
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@book{2005b,
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 title = {{Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining}}
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@proceedings{2005c,
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 title = {{Proceedings International Joint Conference on Neural Networks}}
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@proceedings{2005d,
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@proceedings{2005e,
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@proceedings{2005f,
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@book{2006,
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@proceedings{2006b,
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 title = {{Proceedings of the 23rd international conference on Machine learning}}
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@proceedings{2006c,
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 title = {{3rd International Symposium on Neural Networks}}
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@book{2006d,
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@proceedings{2007,
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 title = {{Proceedings of the 2007 IEEE Symposium on Computational~Intelligence and Datamining (CIDM~2007)}}
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@proceedings{2007b,
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 title = {{Proceedings of the 20th International Congress {\&} Exhibition on Condition Monitoring and Diagnostic Engineering Management}}
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@book{2007c,
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@book{2007d,
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@proceedings{2007e,
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@proceedings{2007f,
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@proceedings{2008,
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@proceedings{2008b,
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@proceedings{2008c,
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@book{2008d,
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@book{2008e,
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@book{2008f,
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@book{2008g,
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 title = {{Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining}}
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@proceedings{2008h,
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 title = {{Eighth IEEE~International Conference on Data Mining}}
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@proceedings{2008i,
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@book{2009,
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@book{2009b,
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@proceedings{2009c,
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 title = {{3rd International Symposium on Advances in Intelligent Data Analysis}}
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@proceedings{2009d,
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@proceedings{2009e,
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 title = {{Proceedings of the 7th IFAC Symposium on Fault Detection, Supervision and Safety of Technical Processes}}
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@book{2010,
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@book{2010b,
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 title = {{Advances in Neural Information Processing Systems 23}}
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@proceedings{2011,
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 title = {{International Conference on Artificial Intelligence and Statistics}}
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@book{2011b,
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@book{2011c,
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@book{2011d,
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@proceedings{2011e,
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@book{2011f,
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 title = {{roceedings of the EARSeL 7th SIG-Imaging Spectroscopy Workshop}}
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@proceedings{2011g,
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 title = {{2011 IEEE International Conference on Robotics and Automation}}
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@book{2012,
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 title = {{2012 IEEE 12th International Conference on Data Mining}}
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@book{2012b,
 year = {2012},
 title = {{2012 IEEE 28th International Conference on Data Engineering (ICDE)}}
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@book{2012c,
 year = {2012},
 title = {{IEEE 12th International Conference on Data Mining (ICDM)}}
}


@book{2012d,
 year = {2012},
 title = {{Proceedings of the IADIS International Conference Intelligent Systems and Agents 2012 and European Conference Data Mining}}
}


@book{2012e,
 year = {2012},
 title = {{IEEE 12th International Conference on Data Mining (ICDM)}}
}


@book{2012f,
 year = {2012},
 title = {{Proceedings of the 7th Vienna International Conference on Mathematical Modelling (MATHMOD 2012)}}
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@book{2012g,
 year = {2012},
 title = {{Asian Conference on Machine Learning 2012}}
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@proceedings{2012h,
 year = {2012},
 title = {{2012 IEEE~International Workshop on Machine Learning for Signal Processing}}
}


@proceedings{2012i,
 year = {2012},
 title = {{International Conference on Measurement, Information and Control (MIC)}}
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@proceedings{2013,
 year = {2013},
 title = {{AUTOTESTCON, 2013 IEEE}},
 isbn = {978-1-4673-5681-7}
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@book{2013b,
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 title = {{Proceedings of the WASET International Conference on Machine Intelligence}}
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@book{2013c,
 year = {2013},
 title = {{Proceedings of the WASET International Conference on Vehicular Electronics and Safety 2013}}
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@proceedings{2013d,
 year = {2013},
 title = {{Proc. 15th WASET Int. Conf. Machine Intelligence}}
}


@proceedings{2013e,
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 title = {{Proceedings of the 30th international conference on machine learning (ICML-13)}}
}


@proceedings{2013f,
 year = {2013},
 title = {{2013 IEEE International Conference on Acoustics, Speech and Signal Processing}}
}


@book{2013g,
 year = {2013},
 title = {{2013 IEEE~International Workshop on Machine Learning for Signal Processing}},
 publisher = {IEEE}
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@proceedings{2013h,
 year = {2013},
 title = {{2013 IEEE Intelligent Vehicles Symposium (IV)}}
}


@proceedings{2013i,
 year = {2013},
 title = {{IEEE/RSJ~International Conference on Intelligent Robots and Systems (IROS)}}
}


@book{2014,
 year = {2014},
 title = {{30th International Conference on Data Engineering}}
}


@book{2014b,
 year = {2014},
 title = {{Proceedings of the 14th SIAM International Conference on Data Mining}}
}


@book{2014c,
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 title = {{Proceedings of Big Data Applications and Principles First International Workshop}}
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@proceedings{2014d,
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 title = {{Student-t Processes as Alternatives to Gaussian Processes}},
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@book{2014f,
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 title = {{Economic Analysis of the Digital Economy}},
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@book{2014g,
 year = {2014},
 title = {{Proceedings of the 17th International Conference on Artificial Intelligence and Statistics (AISTATS)}}
}


@proceedings{2014h,
 year = {2014},
 title = {{Advances in Neural Information Processing Systems 2014}}
}


@book{2014i,
 year = {2014},
 title = {{Integrated Communications, Navigation and Surveillance Conference (ICNS)}}
}


@book{2015,
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 title = {{Proceedings of the 32nd International Conference on Machine Learning}}
}


@proceedings{2015b,
 year = {2015},
 title = {{2015 IEEE International Conference on Robotics and Automation (ICRA)}}
}


@book{2016,
 year = {2016},
 title = {{SAE 2016 World Congress}}
}


@proceedings{2016b,
 year = {2016},
 title = {{2016 IEEE Intelligent Vehicles Symposium}}
}


@book{2016c,
 year = {2016},
 title = {{IEEE International Conference on Advanced Intelligent Mechatronics (AIM) 2016}}
}


@proceedings{2016d,
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 title = {{2016 American Control Conference (ACC)}}
}


@proceedings{2016e,
 year = {2016},
 title = {{2016 IEEE Intelligent Vehicles Symposium (IV)}}
}


@proceedings{2016f,
 year = {2016},
 title = {{Proceedings of the 19th International Conference on Artificial Intelligence and Statistics (AISTATS)}}
}


@proceedings{2016g,
 year = {2016},
 title = {{Proceedings of the 33rd International Conference on Machine Learning}},
 address = {New York}
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@proceedings{2016h,
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 title = {{19th International Conference on Information Fusion}}
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@proceedings{2016i,
 year = {2016},
 title = {{14th IEEE~International Conference on Industrial Informatics (INDIN)}}
}


@proceedings{2016j,
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 title = {{6th IEEE International Electric Drives Production Conference}}
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@proceedings{2016k,
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 title = {{Proceedings of the 1st International Workshop on Science, Application and Methods in Industry 4.0}}
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@proceedings{2018,
 year = {2018},
 title = {{IEEE~International Conference on Industrial Technology}}
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@proceedings{2018b,
 year = {2018},
 title = {{Proceedings of the 2018 IEEE International Conference on Industrial Technology (ICIT), to be published}}
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@inproceedings{Abele2016,
 author = {Abele, Sebastian and Weyrich, Michael},
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 booktitle = {{14th IEEE~International Conference on Industrial Informatics (INDIN)}},
 year = {2016}
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@inproceedings{Abersfelder2016,
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 booktitle = {{6th IEEE International Electric Drives Production Conference}},
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@inproceedings{Agamennoni2013,
 author = {Agamennoni, Gabriel and Worrall, Stewart and Ward, James and Nebot, Eduardo},
 title = {{Robust Non-linear Smoothing for Vehicle State Estimation}},
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 year = {2013}
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@inproceedings{Agamennoni2015,
 author = {Agamennoni, Gabriel and Furgale, Paul and Siegwart, Roland},
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@article{Alfes2017,
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@electronic{Allen2014,
 author = {Allen, David},
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 title = {{Accelerating MATLAB Performance: 1001 tips to speed up MATLAB programs}},
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@booklet{Altmannshofer2010,
 author = {Altmannshofer, Simon},
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@inproceedings{Altmannshofer2016,
 author = {Altmannshofer, Simon and Endisch, Christian},
 title = {{Robust Vehicle Mass and Driving Resistance Estimation}},
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 booktitle = {{2016 American Control Conference (ACC)}},
 year = {2016}
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@inproceedings{Altmannshofer2016b,
 author = {Altmannshofer, Simon and Endisch, Christian and Martin, Jan and Gerngro{\ss}, Martin and Limbacher, Reimund},
 title = {{Robust Estimation of Vehicle Longitudinal Dynamics Parameters}},
 pages = {566--571},
 booktitle = {{2016 IEEE Intelligent Vehicles Symposium (IV)}},
 year = {2016}
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 title = {{Matlab, Simulink, Stateflow: Grundlagen, Toolboxen, Beispiele}},
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 author = {Aravkin, Aleksandr and Burke, James V. and Ljung, Lennart and Lozano, Aurelie and Pillonetto, Gianluigi},
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 author = {Ardeshiri, Tohid and {\"O}zkan, Emre and Orguner, Umut and Gustafsson, Fredrik},
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@booklet{Ardeshiri2015b,
 author = {Ardeshiri, Tohid and {\"O}zkan, Emre and Orguner, Umut and Gustafsson, Fredrik},
 year = {2015},
 title = {{Variational Iterations for Smoothing with Unknown Process and Measurement Noise Covariances}},
 address = {Link{\"o}ping, Sweden}
}


@phdthesis{Ardeshiri2015c,
 author = {Ardeshiri, Tohid},
 year = {2015},
 title = {{Analytical Approximations for Bayesian Inference}},
 address = {Link{\"o}ping, Sweden},
 publisher = {Division of Automatic Control, Department of Electrical Engineering},
 school = {{Link{\"o}ping University}}
}


@booklet{Ardeshiri2015d,
 author = {Ardeshiri, Tohid and Nurminen, Henri and Pich{\'e}, Robert and Gustafsson, Fredrik},
 year = {2015},
 title = {{Variational Iterations for Filtering and Smoothing with skew-t measurement noise}},
 address = {Link{\"o}ping, Sweden}
}


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@booklet{ASAM2014,
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}


@article{Azzalini1985,
 author = {Azzalini, Adelchi},
 year = {1985},
 title = {{A Class of distributions which includes the normal ones}},
 pages = {171--178},
 volume = {12},
 number = {2},
 journal = {{Scandinavian Journal of Statistics}}
}


@article{Azzalini2005,
 author = {Azzalini, Adelchi},
 year = {2005},
 title = {{The skew-normal distribution and related multivariate families}},
 pages = {159--188},
 volume = {32},
 number = {2},
 journal = {{Scandinavian Journal of Statistics}}
}


@electronic{Baath2015,
 author = {B{\aa}{\aa}th, Rasmus},
 year = {2015},
 title = {{Probable Points and Credible Intervals, Part 2: Decision Theory}}
}


@article{Bachoc2013,
 author = {Bachoc, Fran{\c{c}}ois},
 year = {2013},
 title = {{Cross Validation and Maximum Likelihood estimations of hyper-parameters of Gaussian processes with model misspecification}},
 number = {66},
 journal = {{Computational Statistics and Data Analysis}}
}


@article{Bahman1996,
 author = {Samimy, Bahman and Rizzoni, Giorgio},
 year = {1996},
 title = {{Mechanical Signature Analysis Using Time-Frequency Signal Processing: Application to Internal Combustion Engine Knock Detection}},
 pages = {1330--1343},
 volume = {84},
 number = {9},
 journal = {{Proceedings of the IEEE}}
}


@book{Baillieul2015,
 year = {2015},
 title = {{Encyclopedia of Systems and Control}},
 address = {London},
 publisher = {Springer-Verlag},
 editor = {Baillieul, J. and Samad, T.}
}


@book{Baleanu2012,
 year = {2012},
 title = {{Advances in wavelet theory and their applications in engineering, physics and technology}},
 address = {Rijeka},
 publisher = {InTech},
 isbn = {978-953-51-0494-0},
 editor = {Baleanu, Dumitru and Aydin, Handan}
}


@book{Bamberg2012,
 author = {Bamberg, G{\"u}nter and Baur, Franz and Krapp, Michael},
 year = {2012},
 title = {{Statistik}},
 address = {M{\"u}nchen},
 edition = {17},
 publisher = {Oldenbourg}
}


@article{Banerjee2006,
 author = {Banerjee, A. and Burlina, P. and Diehl, C.},
 year = {2006},
 title = {{A support vector method for anomaly detection in hyperspectral imagery}},
 pages = {2282--2291},
 volume = {44},
 number = {8},
 journal = {{IEEE Transactions on Geoscience and Remote Sensing}}
}


@inproceedings{Bansal2013,
 author = {Bansal, Abhijit and Muli, Mahendra and Patil, Kunal},
 abstract = {The current product development environment for mechatronic systems is characterized by tight budgets, reduced development times and immense complexity across the industries - aerospace, automotive, commercial vehicles, etc. A huge emphasis is being given to increase the efficiency in product testing and to make the process more productive in increasing quality, while being cost effective. Companies are seeking software testing tools that offer a comprehensive solution that helps in achieving this goal. Automated software testing for both hardware and the software components is one of the ways the industries are trying to gain efficiency in testing. Given the fact that current mechatronic systems are quite complex, have large numbers and types of IO channels, and are implementing distributed real-time control with multiple communication protocols, a test automation tool that incorporates and tests all of the system functionality is highly desirable. In particular, due to the focus on software quality, as more and more functions are added to the network of Electronic Control Units (ECU), a greater emphasis is laid on the requirements for a comprehensive automated testing tool. The efficiency in development and planned increase in reuse of test assets is an important consideration for efficiency gain. Bringing clarity to the test description would improve readability and maintainability of these test assets. This paper shows the lessons learned from a similar evolution in Model-Based Design, moving from handwritten code to model-based graphical executable representation, can be applied to the testing arena to develop better and more efficient tests. To make the testing process efficient, the test tool should be able to not just execute tests, but organize the test results and present them in a manner easily understood by the end user. The user should be easily able to select tests to be executed and visualize execution results of those tests without need of external - ata analysis and reporting tools. A user should also be able to gather data related to test execution for reporting on overall software quality metrics. In the case of real-time embedded systems, the traditional approach for software testing of test execution, data capture, analysis and reporting creates a lot of inefficiency and repeated testing. A new approach to run test evaluation in parallel to test execution, using the so-called real-time testing solution, eliminates these inefficiencies and results in significant gains in testing time and improving utilization of the test infrastructure. Globalized, distributed development environments may lead to organizations adopting a variety of tool chains and test platforms. Therefore, to gain efficiency, it is desired that the test automation solution should offer easy integration with other third-party test platforms and be independent of the hardware to protect the investment in the long run. Basing such integration interface on established standards gives the user freedom to make choices. Some standards, such as HIL API from ASAM, are being proposed for test systems in the automotive industry. The paper reviews applicability and benefits of such standards. Various industry standards, such as automotive functional safety standard ISO 26262, IEC 61508, IEC 62304 and DO-178C, have addressed requirements for software testing and test tools. It is therefore critical that the selected test automation tool provide support for the prescribed methods and support the standard compliant development process. Tool qualification is a critical topic addressed by these standards and is discussed in this paper. Further, increasingly important requirements such as integration with the overall software development process, management of tests and test data, test reusability, hardware platform independence, test development and execution efficiency, etc. are discussed.},
 title = {{Taming Complexity While Gaining Efficiency: Requirements for the Next Generation of Test Automation Tools}},
 url = {http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=6645055},
 pages = {1--6},
 isbn = {978-1-4673-5681-7},
 booktitle = {{AUTOTESTCON, 2013 IEEE}},
 year = {2013}
}


@inproceedings{Barber2007,
 author = {Barber, David and Chiappa, Silvia},
 title = {{Unified inference for variational Bayesian linear Gaussian state-space models}},
 pages = {81--88},
 booktitle = {{Advances in Neural Information Processing Systems}},
 year = {2007}
}


@article{Barber2010,
 author = {Barber, David and Cemgil, A. Taylan},
 year = {2010},
 title = {{Graphical Models for Time Series}},
 pages = {18--28},
 volume = {27},
 number = {6},
 journal = {{IEEE Signal Processing Magazine}}
}


@book{Barber2011,
 year = {2011},
 title = {{Bayesian Time Series Models}},
 address = {Cambridge, UK},
 publisher = {{Cambridge University Press}},
 editor = {Barber, David and Cemgil, A. Taylan and Chiappa, Silvia}
}


@book{Barber2013,
 author = {Barber, David},
 year = {2012},
 title = {{Bayesian Reasoning and Machine Learning}},
 address = {Cambridge, UK},
 edition = {1},
 publisher = {{Cambridge University Press}}
}


@misc{Barkan2016,
 author = {Barkan, Oren and Weill, Jonathan and Averbuch, Amir},
 year = {2016},
 title = {{Gaussian Process Regression for Out-of-Sample Extension}}
}


@book{Barnett1994,
 author = {Barnett, Vic and Toby, Lewis},
 year = {1994},
 title = {{Outliers in Statistical Data}},
 address = {New York},
 edition = {3},
 publisher = {Wiley}
}


@article{Barr1999,
 author = {Barr, Donald R. and Sherrill, E. Todd},
 year = {1999},
 title = {{Mean and variance of truncated normal distributions}},
 pages = {357--361},
 volume = {53},
 number = {4},
 journal = {{The American Statistician}}
}


@book{BarShalom2001,
 author = {Bar-Shalom, Yaakov and Li, X. Rong and Kirubarajan, Thiagalingam},
 year = {2001},
 title = {{Estimation with Applications to Tracking and Navigation: Theory, Algorithms and Software}},
 publisher = {{John Wiley {\&} Sons, Inc.}}
}


@article{Bavdekar2011,
 author = {Bavdekar, Vinay A. and Deshpande, Anjali P. and Patwardhan, Sachin C.},
 year = {2011},
 title = {{Identification of process and measurement noise covariance for state and parameter estimation using extended Kalman filter}},
 volume = {21},
 number = {4},
 journal = {{Journal of Process Control}}
}


@phdthesis{Beal2003,
 author = {Beal, Matthew James},
 year = {2003},
 title = {{Variational algorithms for approximate Bayesian inference}},
 address = {London},
 school = {{University of London}}
}


@incollection{BenGal2005,
 author = {Ben-Gal, Irad},
 title = {{Outlier detection}},
 publisher = {{Kluwer Academic Publishers}},
 editor = {Rockach, L.},
 booktitle = {{Data Mining and Knowledge Discovery Handbook}},
 year = {2005}
}


@article{Bengio2013,
 author = {Bengio, Yoshua and Courville, Aaron and Vincent, Pascal},
 year = {2013},
 title = {{Representation Learning:~A Review and New Perspectives}},
 pages = {1798},
 volume = {35},
 number = {8},
 journal = {{IEEE Transactions on Pattern Analysis and Machine Intelligence}}
}


@article{BenHur2010,
 author = {Ben-Hur, Asa and Weston, J.},
 year = {2010},
 title = {{A User's Guide to Support Vector Machines}},
 pages = {223--239},
 journal = {{Data Mining Techniques for the Life Sciences}}
}


@phdthesis{Berger2012,
 author = {Berger, Benjamin},
 year = {2012},
 title = {{Modeling and Optimization for Stationary Base Engine Calibration}},
 address = {M{\"u}nchen},
 publisher = {Lehrstuhl f{\"u}r Regelungstechnik der Fakult{\"a}t f{\"u}r Maschinenwesen},
 school = {{Technische Universit{\"a}t M{\"u}nchen}}
}


@incollection{Berger2012b,
 author = {Berger, Benjamin and Rauscher, Florian},
 title = {{Robust Gaussian process modelling for engine calibration}},
 pages = {159--164},
 booktitle = {{Proceedings of the 7th Vienna International Conference on Mathematical Modelling (MATHMOD 2012)}},
 year = {2012}
}


@article{Bergstra2012,
 author = {Bergstra, James and Bengio, Yoshua},
 year = {2012},
 title = {{Random Search for Hyper-Parameter Optimization}},
 pages = {281--305},
 volume = {13},
 number = {1},
 journal = {{Journal of Machine Learning Research}}
}


@inproceedings{Bernard2001,
 author = {Bernard, T. and Cerrato-Jay, G. and Dong, J. and Pickering, D. J. and Braner, L. and Davidson, R. and Jay, M.},
 title = {{The Development of a Sound Quality-Based End-of-Line Inspection System for Powered Seat Adjusters}},
 booktitle = {{SAE 2001 World Congress}},
 year = {2001}
}


@article{Bishop1994,
 author = {Bishop, Christopher M.},
 year = {1994},
 title = {{Novelty detection and neural network validation}},
 pages = {217--222},
 volume = {141},
 number = {4},
 journal = {{IEE~Proceedings - Vision, Image and Signal Processing}}
}


@incollection{Bishop2000,
 author = {Bishop, Christopher M. and Tipping, Michael E.},
 title = {{Variational Relevance Vector Machines}},
 pages = {46--53},
 booktitle = {{Proceedings of the Sixteenth Conference on Uncertainty in Artificial Intelligence}},
 year = {2000}
}


@book{Bishop2006,
 author = {Bishop, Christopher M.},
 abstract = {The field of pattern recognition has undergone substantial development over the years. This book reflects these developments while providing a grounding in the basic concepts of pattern recognition and machine learning. It is aimed at advanced undergraduates or first year PhD students, as well as researchers and practitioners.},
 year = {2006},
 title = {{Pattern Recognition and Machine Learning}},
 address = {New York},
 publisher = {Springer}
}


@electronic{BishopOnline,
 author = {Bishop, Christopher M. and McGrogan, Nicholas and Tarassenko, Lionel},
 title = {{Neural Network Training Using Multi-channel Data with Aggregate Labelling}}
}


@booklet{BITKOM2015,
 author = {BITKOM},
 year = {2015},
 title = {{Big Data und Gesch{\"a}ftsmodell-Innovationen in der Praxis: 40+ Beispiele: Leitfaden}}
}


@electronic{Blei2011,
 author = {Blei, David M.},
 year = {2011},
 title = {{Variational Inference}}
}


@electronic{Blei2017,
 author = {Blei, David M. and Kucukelbir, Alp and McAuliffe, Jon D.},
 year = {2017},
 title = {{Variational Inference: A Review for Statisticians}},
 url = {arXiv:1601.00670v5},
 address = {arXiv}
}


@article{Bobrow1985,
 author = {Bobrow, J. E. and Dubowsky, S. and Gibson, J. S.},
 year = {1985},
 title = {{Time-Optimal Control of Robotic Manipulators Along Specified Paths}},
 pages = {3--17},
 volume = {4},
 number = {3},
 journal = {{The International Journal of Robotics Research}}
}


@article{Bordoloi2014,
 author = {Bordoloi, D. J. and Tiwari, Rajiv},
 year = {2014},
 title = {{Support vector machine based optimization of multi-fault classification of gears with evolutionary algorithms from time-frequency vibration data}},
 pages = {1--14},
 volume = {55},
 journal = {{Measurement}}
}


@phdthesis{Borguet2012,
 author = {Borguet, S{\'e}bastien J.},
 year = {2012},
 title = {{Variations on the Kalman filter for enhanced performance monitoring of gas turbine engines}},
 address = {Li{\`e}ge},
 publisher = {Department of Aerospace and Mechanics},
 school = {{Universit{\'e} de Li{\`e}ge}}
}


@inproceedings{Botsch2007,
 author = {Botsch, Michael and Nossek, Josef A.},
 title = {{Feature Selection for Change Detection in Multivariate Time-Series}},
 pages = {590--597},
 booktitle = {{Proceedings of the 2007 IEEE Symposium on Computational~Intelligence and Datamining (CIDM~2007)}},
 year = {2007}
}


@inproceedings{Botsch2008,
 author = {Botsch, Michael and Nossek, Josef A.},
 title = {{Construction of Interpretable Radial Basis Function Classifiers Based on the Random Forest Kernel}},
 pages = {220--227},
 booktitle = {{Neural Networks}},
 year = {2008}
}


@phdthesis{Botsch2009,
 author = {Botsch, Michael},
 year = {2009},
 title = {{Machine Learning Techniques for Time Series Classification}},
 address = {M{\"u}nchen},
 publisher = {Fakult{\"a}t f{\"u}r Elektrotechnik und Informationstechnik},
 school = {{Technische Universit{\"a}t M{\"u}nchen}}
}


@unpublished{Botsch2013,
 author = {Botsch, Michael},
 year = {Wintersemester 2013 - 2014},
 title = {{Signalverarbeitung in der Fahrzeugsicherheit: Vorlesungsskript}},
 address = {Technische Hochschule Ingolstadt},
 note = {{Vorlesungsskript}}
}


@book{Box2005,
 author = {Box, George E. P. and Hunter, John Stuart and Hunter, William Gordon},
 year = {2005},
 title = {{Statistics for Experimenters: Design, Innovation, and Discovery}},
 address = {Hoboken, NJ},
 edition = {2},
 publisher = {{John Wiley {\&} Sons, Inc.}}
}


@book{Box2008,
 author = {Box, George E. P. and Jenkins, Gwilym M. and Reinsel, Gregory C.},
 year = {2008},
 title = {{Time Series Analysis: Forecasting and Control}},
 address = {Hoboken, NJ},
 edition = {4. ed},
 publisher = {Wiley},
 isbn = {978-1-118-61919-3},
 series = {{Wiley series in probability and statistics}}
}


@book{Boyd2004,
 author = {Boyd, Stephen P. and Vandenberghe, Lieven},
 abstract = {From the publisher. Convex optimization problems arise frequently in many different fields. This book provides a comprehensive introduction to the subject, and shows in detail how such problems can be solved numerically with great efficiency. The book begins with the basic elements of convex sets and functions, and then describes various classes of convex optimization problems. Duality and approximation techniques are then covered, as are statistical estimation techniques. Various geometrical problems are then presented, and there is detailed discussion of unconstrained and constrained minimization problems, and interior-point methods. The focus of the book is on recognizing convex optimization problems and then finding the most appropriate technique for solving them. It contains many worked examples and homework exercises and will appeal to students, researchers and practitioners in fields such as engineering, computer science, mathematics, statistics, finance, and economics.},
 year = {2004},
 title = {{Convex Optimization}},
 address = {Cambridge, UK and New York},
 publisher = {{Cambridge University Press}},
 isbn = {978-0-521-83378-3}
}


@phdthesis{Bradford2006,
 author = {Bradford, Samuel Case},
 year = {2006},
 title = {{Time-Frequency Analysis of Systems with Changing Dynamic Properties}},
 address = {Pasadena},
 school = {{California Institute of Technology}}
}


@article{Bradley1997,
 author = {Bradley, Andrew P.},
 year = {1997},
 title = {{The use of the area under the ROC curve in the evaluation of machine learning algorithms}},
 pages = {1145--1159},
 volume = {30},
 number = {7},
 journal = {{Pattern Recognition}}
}


@article{Breiman1996,
 author = {Breiman, Leo},
 year = {1996},
 title = {{Bagging Predictors}},
 pages = {123--140},
 volume = {24},
 number = {2},
 journal = {{Machine Learning}}
}


@article{Breiman2001,
 author = {Breiman, Leo},
 year = {2001},
 title = {{Statistical modeling: the two cultures}},
 pages = {1999-231},
 volume = {16},
 number = {3},
 journal = {{Statistical Science}}
}


@article{Breunig2000,
 author = {Breunig, Markus M. and Kriegel, Hans-Peter and Ng, Raymond T. and Sander, J{\"o}rg},
 year = {2000},
 title = {{LOF: identifying density-based local outliers}},
 pages = {93--104},
 volume = {29},
 number = {2},
 journal = {{ACM Sigmod Record}}
}


@article{Brodersen2015,
 author = {Brodersen, Kay H. and Gallusser, Fabian and Koehler, Jim and Remy, Nicolas and Scott, Steven L.},
 year = {2015},
 title = {{Inferring Causal Impact Using Bayesian Structural Time-Series Models}},
 pages = {247--274},
 volume = {9},
 number = {1},
 journal = {{The Annals of Applied Statistics}}
}


@book{Bronstein2013,
 author = {Bronstein, I. N. and Semendjaew, K. A.},
 year = {2013},
 title = {{Springer-Taschenbuch der Mathematik}},
 address = {Wiesbaden},
 edition = {3},
 publisher = {{Springer Spektrum}}
}


@phdthesis{Brummet2006,
 author = {Brummet, Klaus},
 year = {2006},
 title = {{Analyse, Optimierung und Interpretation der K{\"o}rper- und Luftschallmessungen an Abgasturboladern von Ottomotoren im Kalttest}},
 address = {Ingolstadt, Deutschland},
 publisher = {Fachbereich Maschinenbau},
 school = {{Fachhochschule Ingolstadt}}
}


@phdthesis{Brunet2010,
 author = {Brunet, Florent},
 year = {2010},
 title = {{Contributions to Parametric Image Registration and 3D Surface Reconstruction}},
 school = {{Universit{\'e} d' Auvergne}}
}


@electronic{Brunet2011,
 author = {Brunet, Florent},
 year = {2011},
 title = {{M-Estimator}},
 url = {http://www.brnt.eu/phd/node13.html},
 urldate = {29.04.2016}
}


@incollection{Bryant1999,
 author = {Bryant, Bruce D. and Marko, Kenneth A.},
 title = {{Case Example 2: Data analysis for diagnostics and process monitoring of automotive engines}},
 publisher = {{Springer US}},
 editor = {Lee, Jay and Wang, Ben},
 booktitle = {{Computer-aided Maintenance}},
 year = {1999}
}


@article{Bryll2003,
 author = {Bryll, Robert and Gutierrez-Osuna, Ricardo and Quek, Francis},
 year = {2003},
 title = {{Attribute bagging: improving accuracy of classier ensembles by using random feature subsets}},
 pages = {1291--1302},
 volume = {36},
 number = {6},
 journal = {{Pattern Recognition}}
}


@article{Budack2016,
 author = {Budack, Ralf and Wurms, Rainer and Mendl, G{\"u}nther and Heiduk, Thomas},
 year = {2016},
 title = {{Der neue 2,0-l-R4-TFSI-Motor von Audi}},
 pages = {16--25},
 volume = {77},
 number = {5},
 journal = {{MTZ-Motortechnische Zeitschrift}}
}


@article{Burges1998,
 author = {Burges, Christopher J.C.},
 year = {1998},
 title = {{A Tutorial on Support Vector Machines for Pattern Recognition}},
 pages = {121--167},
 volume = {2},
 number = {2},
 journal = {{Data Mining and Knowledge Discovery}}
}


@article{Burman1965,
 author = {Burman, J. Peter},
 year = {1965},
 title = {{Moving seasonal adjustment of economic time series}},
 pages = {534--558},
 volume = {128},
 number = {4},
 journal = {{Journal of the Royal Statistical Society. Series A (General)}}
}


@article{Burman1994,
 author = {Burman, Prabir},
 year = {1994},
 title = {{A cross-validation method for dependent data}},
 pages = {351--358},
 volume = {81},
 number = {2},
 journal = {{Biometrika}}
}


@book{Butz2012,
 author = {Butz, Tilman},
 year = {2012},
 title = {{Fouriertransformation f{\"u}r Fu{\ss}g{\"a}nger}},
 address = {Wiesbaden},
 edition = {7., aktualisierte Auflage},
 publisher = {{Vieweg+Teubner Verlag / Springer Fachmedien Wiesbaden GmbH, Wiesbaden}},
 isbn = {978-3-8348-0946-9},
 series = {{SpringerLink : B{\"u}cher}}
}


@inproceedings{Campbell2000,
 author = {Campbell, Colin and Bennett, Kristin P.},
 title = {{A Linear Programming Approach to Novelty Detection}},
 pages = {395},
 booktitle = {{Proceedings of the Advances in Neural Information Processing Systems}},
 year = {2000}
}


@book{Cappe2005,
 author = {Capp{\'e}, Olivier and Moulines, Eric and Ryd{\'e}n, Tobias},
 year = {2005},
 title = {{Inference in Hidden Markov Models}},
 address = {New York},
 publisher = {{Springer Science+Business Media}}
}


@inproceedings{Cappe2005b,
 author = {Capp{\'e}, Olivier and Moulines, Eric},
 title = {{Recursive computation of the score and observed information matrix in hidden markov models}},
 pages = {703--708},
 booktitle = {{Statistical Signal Processing, 2005 IEEE/SP 13th Workshop on}},
 year = {2005}
}


@inproceedings{Carbery2018,
 author = {Carbery, Caoimhe M. and Woods, Roger and Marshall, Adele H.},
 title = {{A Bayesian network based learning system for modelling faults in large-scale manufacturing}},
 pages = {1357--1362},
 booktitle = {{Proceedings of the 2018 IEEE International Conference on Industrial Technology (ICIT), to be published}},
 year = {2018}
}


@article{Carlsson2009,
 author = {Carlsson, Lars and Helgee, Ernst Ahlberg and Boyer, Scott},
 year = {2009},
 title = {{Interpretation of Nonlinear QSAR Models Applied to Ames Mutagenicity Data}},
 pages = {2551--2558},
 volume = {49},
 number = {11},
 journal = {{J. Chem. Inf. Model.}}
}


@article{Cawley2010,
 author = {Cawley, Gavin C. and Talbot, Nicola L. C.},
 year = {2010},
 title = {{On Over-fitting in Model Selection and Subsequent Selection Bias in Performance Evaluation}},
 pages = {2079--2107},
 volume = {11},
 journal = {{Journal of Machine Learning Research}}
}


@book{Chacon,
 author = {Chacon, Scott and Straub, Ben},
 title = {{Pro Git}},
 edition = {2}
}


@book{Chacon2009,
 author = {Chacon, Scott},
 year = {2009},
 title = {{Pro Git}}
}


@article{Chan2007,
 author = {Chan, Tao and Morris, Julian and Martin, Elaine},
 year = {2007},
 title = {{Gaussian process regression for multivariate spectroscopic calibration}},
 pages = {59--71},
 volume = {87},
 number = {1},
 journal = {{Chemometrics and Intelligent Laboratory Systems}}
}


@booklet{Chan2013,
 author = {Chan, Antoni B.},
 year = {2013},
 title = {{Multivariate generalized Gaussian process models}}
}


@booklet{Chandola2007,
 author = {Chandola, Varun and Banerjee, Arindam and Kumar, Vipin},
 year = {2007},
 title = {{Anomaly Detection: A Survey}}
}


@inproceedings{Chandola2008,
 author = {Chandola, Varun and Mithal, Varun and Kumar, Vipin},
 title = {{Comparative evaluation of anomaly detection techniques for sequence data}},
 pages = {743--748},
 booktitle = {{2008 Eigth IEEE International Conference on Data Mining}},
 year = {2008}
}


@phdthesis{Chandola2009,
 author = {Chandola, Varun},
 year = {2009},
 title = {{Anomaly Detection for Symbolic Sequences and Time Series Data}},
 school = {{University of Minnesota}}
}


@article{Chandola2009b,
 author = {Chandola, Varun and Banerjee, Arindam and Kumar, Vipin},
 year = {2009},
 title = {{Anomaly detection: a survey}},
 volume = {41},
 number = {3},
 journal = {{ACM Computing Surveys}}
}


@article{Chang2006,
 author = {Chang, Fay and Dean, Jeffrey and Ghemawat, Sanjay and Hsieh, Wilson C. and Wallach, Deborah A. and Burrows, Mike and Chandra, Tushar and Fikes, Andrew and Gruber, Robert E.},
 title = {{Bigtable: A Distributed Storage System for Structured Data}},
 volume = {2006}
}


@phdthesis{Chang2013,
 author = {Chang, Wei-Cheng and Lee, Ching-Pei and Lin, Chih-Jen},
 year = {2013},
 title = {{A revisit to support vector data description}},
 address = {Taipei, Taiwan},
 school = {{National Taiwan University}}
}


@book{Chapra2008,
 author = {Chapra, Steven C.},
 year = {2008},
 title = {{Applied numerical methods with MATLAB for engineers and scientists: Includes bibliographical references (S. 579) and index}},
 address = {Boston, Mass.},
 edition = {2. ed},
 publisher = {{McGraw-Hill Higher Education}},
 isbn = {978-0-07-313290-7}
}


@article{Chatterjee1986,
 author = {Chatterjee, Samprit and Hadi, Ali S.},
 year = {1986},
 title = {{Influential Observations, High Leverage Points, and Outliers in Linear Regression}},
 pages = {379--393},
 volume = {1},
 number = {3},
 journal = {{Statistical Science}}
}


@article{Chawla2008,
 author = {Chawla, Nitesh V. and Cieslak, David A. and Hall, Lawrence O. and Joshi, Ajay},
 year = {2008},
 title = {{Automatically countering imbalance and its empirical relationship to cost}},
 pages = {225--252},
 volume = {17},
 number = {2},
 journal = {{Data Mining and Knowledge Discovery}}
}


@book{Chen1999,
 author = {Chen, Jie and Patton, Ron},
 year = {1999},
 title = {{Robust model-based fault diagnosis for dynamic systems}},
 address = {Boston},
 volume = {3},
 publisher = {Kluwer},
 isbn = {0-7923-8411-3},
 series = {{The Kluwer international series on Asian studies in computer and information science}}
}


@booklet{Chen2003,
 author = {Chen, Zhe},
 year = {2003},
 title = {{Bayesian filtering: From Kalman filters to particle filters, and beyond}}
}


@booklet{ChenTutorial,
 author = {Chen, Pai-Hsuen and Lin, Chih-Jen and Sch{\"o}lkopf, Bernhard},
 title = {{A tutorial on nu-support vector machines}}
}


@article{Cheplygina2011,
 author = {Cheplygina, Veronika and Tax, David Martinus Johannes},
 year = {2011},
 title = {{Pruned random subspace method for one-class classifiers}},
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 abstract = {The Hilbert transform{\textless}tex{\textgreater}H{f(t)}{\textless}/tex{\textgreater}of a given waveform{\textless}tex{\textgreater}f(t){\textless}/tex{\textgreater}is defined with the convolution{\textless}tex{\textgreater}H{f(t)} = f(t) ast (1/pit){\textless}/tex{\textgreater}. It is well known that the second type of Hilbert transform{\textless}tex{\textgreater}K{\_}{0}{f(x)}=phi(x) ast (1/2pi)cotfrac{1}{2}x{\textless}/tex{\textgreater}exists for the transformed function{\textless}tex{\textgreater}f(tgfrac{1}{2}x)= phi(x){\textless}/tex{\textgreater}. If the function{\textless}tex{\textgreater}f(t){\textless}/tex{\textgreater}is periodic, it can be proved that one period of the{\textless}tex{\textgreater}H{\textless}/tex{\textgreater}transform of{\textless}tex{\textgreater}f(t){\textless}/tex{\textgreater}is given by the H{\textless}inf{\textgreater}1{\textless}/inf{\textgreater}transform of one period of{\textless}tex{\textgreater}f(t){\textless}/tex{\textgreater}without regard to the scale of tbe variable. On the base of the discrete Fourier transform (DFT), the discrete Hilbert transform (DHT) is introduced and the defining expression for it is given. It is proved that this expression of DHT is identical to the relation obtained by the use of the trapezoidal rule to the cotangent form of the Hilbert transform.},
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}


@booklet{IEEE0,
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 title = {{How to write for technical periodicals {\&} conferences}}
}


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@booklet{IEEE2014,
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}


@booklet{IEEE2015,
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@booklet{IEEEb,
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@booklet{IEEEc,
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@booklet{IEEEd,
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@booklet{IEEEe,
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@booklet{IEEEf,
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}


@phdthesis{Ingelfinger,
 author = {Ingelfinger, U.},
 title = {{Einf{\"u}hrung in MATLAB~und SIMULINK}}
}


@electronic{Internet,
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@misc{Isermann,
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 title = {{Literatur{\"u}bersicht und Signal Analysis Methods: Scans}}
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 title = {{Supervision, Fault-Detection and Fault-Diagnosis Methods - An Introduction}},
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@book{Isermann2003,
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 year = {2003},
 title = {{Modellgest{\"u}tzte Steuerung, Regelung und Diagnose von Verbrennungsmotoren: Mit 26 Tabellen}},
 address = {Berlin},
 publisher = {Springer},
 isbn = {3-540-44286-3}
}


@book{Isermann2003b,
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 year = {2003},
 title = {{Mechatronic Systems}},
 address = {London},
 publisher = {Springer}
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@article{Isermann2005,
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 journal = {{Annual Reviews in Control}}
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@book{Isermann2006,
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 year = {2006},
 title = {{Fault-Diagnosis Systems: An Introduction from Fault Detection to Fault Tolerance}},
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 publisher = {Springer}
}


@book{Isermann2010,
 year = {2010},
 title = {{Elektronisches Management motorischer Fahrzeugantriebe: Elektronik, Modellbildung, Regelung und Diagnose f{\"u}r Verbrennungsmotoren, Getriebe und Elektroantriebe ; mit 24 Tabellen}},
 address = {Wiesbaden},
 edition = {1. Aufl},
 publisher = {{Vieweg + Teubner}},
 isbn = {978-3-8348-0855-4},
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 editor = {Isermann, Rolf}
}


@book{Isermann2011,
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 title = {{Fault-Diagnosis Applications: Model-Based Condition Monitoring: Actuators, Drives, Machinery, Plants, Sensors, and Fault-Tolerant Systems}},
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 publisher = {Springer},
 isbn = {978-3-642-12766-3}
}


@book{Isermann2011b,
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 year = {2011},
 title = {{Identification of Dynamic Systems: An Introduction with Applications}},
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 publisher = {Springer},
 isbn = {9783540788782}
}


@book{Isermann2014,
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 year = {2014},
 title = {{Engine Modeling and Control}},
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 publisher = {{Springer Berlin Heidelberg}},
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}


@book{Isermann2017,
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 year = {2017},
 title = {{Combustion Engine Diagnosis: Model-based Condition Monitoring of Gasoline and Diesel Engines and their Components}}
}


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@article{Jack2002,
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}


@phdthesis{Jacobsen2008,
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 title = {{Benchmarking Endkontrollen in der mechanischen Fertigung}},
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@book{Karrenberg2013,
 author = {Karrenberg, U.},
 abstract = {This is a very new concept for learning Signal Processing, not only from the physically-based scientific fundamentals, but also from the didactic perspective, based on modern results of brain research. The textbook together with the DVD form a learning system that provides investigative studies and enables the reader to interactively visualize even complex processes. The unique didactic concept is built on visualizing signals and processes on the one hand, and on graphical programming of signal processing systems on the other. The concept has been designed especially for microelectronics, computer technology and communication. The book allows to develop, modify, and optimize useful applications using DasyLab - a professional and globally supported software for metrology and control engineering. With the 3rd edition, the software is also suitable for 64 bit systems running on Windows 7. Real signals can be acquired, processed and played on the sound card of your computer. The book provides more than 200 pre-programmed signal engineering systems and design transparencies. Numerous introduction-videos, one for every chapter, and more than 250 high-quality figures come along with the learning system, as well as a visualization of all the {\dq}living{\dq} experiments and their results. With this learning system, readers can now make use of {\dq}equipment{\dq} and software. The new, 3rd edition many improvements, corrections and smaller additions, and a new chapter new chapter presenting a clear introduction to mathematical modeling of signals, processes, and systems.},
 year = {2013},
 title = {{Signals, Processes, and Systems: An Interactive Multimedia Introduction to Signal Processing}},
 address = {Berlin and New York},
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 publisher = {Springer},
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}


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@book{Kohler2012,
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 title = {{Verbrennungsmotoren: Motormechanik, Berechnung und Auslegung es Hubkolbenmotors}},
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 year = {2006},
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@article{Kostyszyn2017,
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@book{Kotz2004,
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 year = {2004},
 title = {{Multivariate t Distributions and Their Applications}},
 address = {Cambridge, UK},
 publisher = {{Cambridge University Press}}
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@book{Kotz2006,
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 title = {{Encyclopedia of Statistical Sciences}},
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 editor = {Kotz, Samuel and Read, Campbell B. and Balakrishnan, N. and Vidakovic, Brani}
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@incollection{Kriegel2012,
 author = {Kriegel, Hans-Peter and Kr{\"o}ger, Peer and Schubert, Erich and Zimek, Arthur},
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 booktitle = {{2012 IEEE 12th International Conference on Data Mining}},
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@booklet{Krogstad2004,
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 title = {{How to use the MATLAB FFT2-routines}}
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@book{Kruschke2015,
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 year = {2015},
 title = {{Doing Bayesian Data Analysis}},
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@phdthesis{Ku2006,
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 title = {{Gaussian Process Models for Robust Regression, Classification, and Reinforcement Learning}},
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 publisher = {Fachbereich Informatik},
 school = {{Technische Universit{\"a}t Darmstadt}}
}


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@electronic{Kucukelbir2016,
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 address = {arXiv:1603.00788v1}
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@book{Kuhn2013,
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 year = {2013},
 title = {{Applied Predictive Modeling}},
 address = {New York},
 publisher = {{Springer Science+Business Media}}
}


@misc{Kumar2015,
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 year = {2015},
 title = {{Identifying Rare Class in Absence of True Labels}}
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@article{Kwon2005,
 author = {Kwon, Heesung. and Nasrabadi, Nasser M.},
 year = {2005},
 title = {{Kernel RX-algorithm: a nonlinear anomaly detector for hyperspectral imagery}},
 pages = {388--397},
 volume = {43},
 number = {2},
 journal = {{IEEE Transactions on Geoscience and Remote Sensing}}
}


@phdthesis{Laitl2012,
 author = {Laitl, Matej},
 year = {2012}
}


@article{Lambert2017,
 author = {Lambert, Simon M. and Armstrong, Matthew and Attidekou, Pierrot S. and Christensen, Paul A. and Widmer, James D. and Wang, Chen and Scott, Keith},
 year = {2017},
 title = {{Rapid Nondestructive-Testing Technique for In-Line Quality Control of Li-Ion Batteries}},
 pages = {4017--4026},
 volume = {64},
 number = {5},
 journal = {{IEEE Transactions on Industrial Electronics}}
}


@inproceedings{Lanckriet2002,
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 title = {{Robust Novelty Detection with Single-Class MPM}},
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 year = {2002}
}


@book{Landau2011,
 author = {Landau, Ioan Dor{\'e} and Lozano, Rogelio and M'Saad, Mohammed and Karimi, Alireza},
 year = {2011},
 title = {{Adaptive Control: Algorithms, Analysis and Applications}},
 address = {London},
 edition = {2},
 publisher = {Springer-Verlag}
}


@phdthesis{Landsmann2014,
 author = {Landsmann, Peter},
 year = {2014},
 title = {{Sensorless Control of Synchronous Machines by Linear Approximation of Oversampled Current}},
 address = {M{\"u}nchen},
 publisher = {Fakult{\"a}t f{\"u}r Elektrotechnik und Informationstechnik},
 school = {{Technische Universit{\"a}t M{\"u}nchen}}
}


@article{Lange1989,
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 year = {1989},
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 author = {Laskov, Pavel and Sch{\"a}fer, Christin and Kotenko, Igor},
 year = {2004},
 title = {{Intrusion detection in unlabeled data with quarter-sphere Support Vector Machines}},
 pages = {228--236},
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 journal = {{Praxis der Informationsverarbeitung und Kommunikation}}
}


@misc{Lauritzen2009,
 author = {Lauritzen, Steffen},
 year = {2009},
 title = {{Further Statistical Inference: BS2}},
 address = {University of Oxford}
}


@booklet{Lauritzen2009a,
 author = {Lauritzen, Steffen},
 year = {23.02.2009},
 title = {{Wishart and Inverse Wishart Distributions}}
}


@incollection{Lazarevic2005,
 author = {Lazarevic, Aleksandar and Kumar, Vipin},
 title = {{Feature Bagging for Outlier Detection}},
 pages = {157--166},
 booktitle = {{Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining}},
 year = {2005}
}


@book{Lee1999,
 abstract = {In today's business environment, reliability and maintenance drastically affect the three key elements of competitiveness - quality, cost, and product lead time. Well-maintained machines hold tolerances better, help reduce scrap and rework, and raise consistency and quality of the part in addition to cutting total production costs. Today, many factories are still performing maintenance on equipment in a reactive manner due to a lack of understanding about machine performance behaviour. To improve production efficiency, computer-aided maintenance and diagnostic methodology must be applied effectively in manufacturing. This book focuses on the fundamental principles of predictive maintenance and diagnostic engineering. In addition to covering the relevant theory, techniques and methodologies in maintenance engineering, the book also provides numerous case studies and examples illustrating the successful application of the principles and techniques outlined.},
 year = {1999},
 title = {{Computer-aided Maintenance: Methodologies and Practices}},
 address = {Boston, MA},
 publisher = {{Springer US}},
 editor = {Lee, Jay and Wang, Ben}
}


@book{Lee2007,
 author = {Lee, John A. and Verleysen, Michel},
 year = {2007},
 title = {{Nonlinear Dimensionality Reduction}},
 publisher = {Springer},
 isbn = {978-0-387-39350-6}
}


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 author = {Lee, Kiyoung and Kim, Dae-Won and Lee, Kwang H. and Lee, Doheon},
 abstract = {The purpose of data description is to give a compact description of the target data that represents most of its characteristics. In a support vector data description (SVDD), the compact description of target data is given in a hyperspherical model, which is determined by a small portion of data called support vectors. Despite the usefulness of the conventional SVDD, however, it may not identify the optimal solution of target description especially when the support vectors do not have the overall characteristics of the target data. To address the issue in SVDD methodology, we propose a new SVDD by introducing new distance measurements based on the notion of a relative density degree for each data point in order to reflect the distribution of a given data set. Moreover, for a real application, we extend the proposed method for the protein localization prediction problem which is a multiclass and multilabel problem. Experiments with various real data sets show promising results.},
 year = {2007},
 title = {{Density-Induced Support Vector Data Description}},
 pages = {284--289},
 volume = {18},
 number = {1},
 journal = {{IEEE transactions on neural networks}}
}


@incollection{Leitner2016,
 author = {Leitner, Lukas and Lagrange, Antoine and Endisch, Christian},
 title = {{End-of-line fault detection for combustion engines using one-class classification}},
 pages = {207--213},
 booktitle = {{IEEE International Conference on Advanced Intelligent Mechatronics (AIM) 2016}},
 year = {2016}
}


@inproceedings{Leitner2018,
 author = {Leitner, Lukas and Endisch, Christian},
 title = {{Robust Stochastic Process Models and Parameter Estimation for Industrial End-of-Line-Testing}},
 booktitle = {{Proceedings of the 2018 IEEE International Conference on Industrial Technology (ICIT)}}
}


@inproceedings{Leitner2018b,
 author = {Leitner, Lukas and Endisch, Christian},
 title = {{Robust stochastic process models and parameter estimation for industrial end-of-line-testing}},
 pages = {1520--1525},
 booktitle = {{Proceedings of the 2018 IEEE International Conference on Industrial Technology (ICIT), to be published}},
 year = {2018}
}


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@incollection{Li2009,
 author = {Li, Xiao-Li and Yu, Philip S. and Liu, Bing and Ng, See-Kiong},
 title = {{Positive Unlabeled Learning for Data Stream Classification}},
 pages = {257--268},
 booktitle = {{2009 SIAM International Conference on Data Mining}},
 year = {2009}
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}


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 title = {{A Symbolic Representation of Time Series, with Implications for Streaming Algorithms}},
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 booktitle = {{Proceedings of the 8th ACM~SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery}},
 year = {2003}
}


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@electronic{LinkopingsUniversitet,
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 author = {Liu, Bing and Dai, Yang and {Wee Sun Lee, Xiaoli Li} and Yu, Philip S.},
 title = {{Building Text Classifiers Using Positive and Unlabeled Examples}},
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 year = {2003}
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 year = {2008}
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 volume = {38},
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}


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 number = {3},
 journal = {{Knowledge and Information Systems}}
}


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 abstract = {Multi-Layer-Perceptron Netze, Parit{\"a}tsgleichung mit Hammersteinmodelle},
 year = {2000},
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}


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}


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 year = {2003}
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@book{Ma2014,
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 title = {{Optimizing support vector data description by automatically generating outliers}},
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 year = {2011}
}


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 title = {{Filtersynthese zur simultanen Minimierung von Existenz-, Assoziations- und Zustandsunsicherheiten in der  Fahrzeugumfelderfassung mit heterogenen Sensordaten}},
 school = {{Universit{\"a}t Ulm}}
}


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 title = {{Modeling and Simulation of Non-Stationary Vehicle Vibration Signals Based on Hilbert Spectrum}},
 journal = {{Mechanical Systems and Signal Processing}}
}


@misc{Marchini2013,
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 journal = {{Signal Processing}}
}


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 volume = {83},
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 journal = {{Signal Processing}}
}


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@phdthesis{Martin2016,
 author = {Martin, Jan},
 year = {2016},
 title = {{Parametrierung und Bewertung eines Fahrwiderstandssch{\"a}tzers durch statistische Messdatenanalyse}},
 address = {Ingolstadt},
 school = {{Technische Hochschule Ingolstadt}}
}


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@incollection{Masud2011,
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 title = {{Detecting Recurring and Novel Classes in Concept-Drifting Data Streams}},
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 year = {2011}
}


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 year = {2012},
 title = {{Classification and Adaptive Novel Class Detection of Feature-Evolving Data Streams}},
 journal = {{IEEE Transactions on Knowledge and Data Engineering}}
}


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@unpublished{Mazzoni,
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 title = {{Zeitreihenanalyse: Einstieg und Aufgaben}}
}


@book{McElreath2015,
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 title = {{Statistical Rethinking: A Bayesian Course with Examples in R and Stan}},
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 publisher = {{Chapman {\&} Hall / CRC}}
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}


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 journal = {{Statistics Surveys}}
}


@book{McLachlan2008,
 author = {McLachlan, Geoffrey J. and Krishnan, Thriyambakam},
 year = {2008},
 title = {{The EM~Algorithm and Extensions}},
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@misc{Meeds,
 author = {Meeds, Edward and Osindero, Simon},
 title = {{An Alternative Infinite Mixture Of Gaussian Process Experts}}
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 author = {Meier, Benedikt},
 year = {1992},
 title = {{Der Endfunktionstest zur Qualit{\"a}ts- und Funktionspr{\"u}fung bei der Montage von Verbrennungsmotoren}},
 address = {Hannover},
 publisher = {Fakult{\"a}t f{\"u}r Maschinenwesen},
 school = {{Universit{\"a}t Hannover}}
}


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 year = {1989},
 title = {{Robustification of Kalman Filter Models}},
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@phdthesis{Menzel2007,
 author = {Menzel, Lars},
 year = {2007},
 title = {{M{\"o}glichkeiten der Pr{\"u}fung von Verbrennungsmotoren -- Kalttest, Hottest und Leistungsbremse}},
 publisher = {Institut f{\"u}r Produktionstechnik},
 school = {{Fachhochschule Braunschweig/Wolfenb{\"u}ttel}}
}


@book{Merker2012,
 author = {Merker, G{\"u}nter P. and Schwarz, Christian and Teichmann, R{\"u}diger},
 year = {2012},
 title = {{Grundlagen Verbrennungsmotoren: Funktionsweise, Simulation, Messtechnik}},
 edition = {6}
}


@book{Merker2014,
 author = {Merker, G{\"u}nter P. and Teichmann, R{\"u}diger},
 year = {2014},
 title = {{Grundlagen Verbrennungsmotoren: Funktionsweise, Simulation, Messtechnik}},
 edition = {7}
}


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 author = {Mertins, Alfred},
 year = {2013},
 title = {{Signaltheorie: Grundlagen der Signalbeschreibung, Filterb{\"a}nke, Wavelets, Zeit-Frequenz-Analyse, Parameter- und Signalsch{\"a}tzung}},
 address = {Wiesbaden},
 edition = {3., {\"u}berarb. und erweiterte Aufl},
 publisher = {{Springer Vieweg}},
 isbn = {978-3-8348-1394-7},
 series = {{SpringerLink : B{\"u}cher}}
}


@phdthesis{Merwe2004,
 author = {{van der Merwe}, Rudolph},
 year = {2004},
 title = {{Sigma-Point Kalman Filters for Probabilistic Inference in Dynamic State-Space Models}},
 school = {{University of Stellenbosch}}
}


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 title = {{Basic principles of ROC analysis}},
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 author = {Meurant, G{\'e}rard},
 year = {2008},
 title = {{Tridiagonal Matrices}}
}


@phdthesis{Meyer2014,
 author = {Meyer, Michael Josef},
 year = {2014},
 title = {{Interpretation und Optimierung mechanischer Pr{\"u}fmerkmale im Kalttest}},
 address = {Ingolstadt, Deutschland},
 publisher = {Fakult{\"a}t Maschinenbau},
 school = {{Technische Hochschule Ingolstadt}}
}


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 title = {{Robust Hyperspectral Classification Using Relevance Vector Machine}},
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@booklet{Minka2000,
 author = {Minka, Thomas P.},
 year = {28.06.2000},
 title = {{Variational Bayes for mixture models: reversing EM}}
}


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 author = {Minka, Thomas P.},
 title = {{Expectation propagation for approximate Bayesian inference}},
 pages = {362--369},
 publisher = {{Morgan Kaufmann Publishers Inc.}},
 booktitle = {{Proceedings of the Seventeenth Conference on Uncertainty in Artificial Intelligence}},
 year = {2001}
}


@phdthesis{Minka2001c,
 author = {Minka, Thomas P.},
 year = {2001},
 title = {{A Family of Algorithms for Approximate Bayesian Inference}},
 school = {{Massachusetts Institute of Technology}}
}


@book{Mitsa2010,
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 year = {2010},
 title = {{Temporal Data Mining}},
 publisher = {{Chapman {\&} Hall /~CRC}}
}


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 title = {{Foundations of Machine Learning}},
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@booklet{Moser2016,
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 title = {{How to typeset equations in Latex}}
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 author = {M{\"u}ller, Emmanuel and Assent, Ira and Iglesias, Patricia and M{\"u}lle, Yvonne and B{\"o}hm, Klemens},
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@phdthesis{Murphy2002,
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Ist bei Givaudan (Head of a global network of manufacturing engineering departments)

War bei W{\"a}rtsil{\"a} (Head of a global network of manufacturing engineering / manufacturing technology departments located in China, Korea, Switzerland, Netherlands and Finland)

ThyssenKrupp (designing and building of production plants and testing plants with the respective mechanical and chemical equipment inside)

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 year = {2009}
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@book{Rasch2009b,
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@article{Rhode2016,
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 title = {{Fertigungstechnische Optimierung elektrischer Drehfeldstatoren}},
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 year = {2013}
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@booklet{Roth2013c,
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 year = {2013},
 title = {{On the multivariate t distribution}},
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@phdthesis{Roth2013d,
 author = {Roth, Michael},
 year = {2013},
 title = {{Kalman Filters for Nonlinear Systems and Heavy-Tailed Noise}},
 address = {Link{\"o}ping},
 publisher = {Division of Automatic Control},
 school = {{Link{\"o}ping University}}
}


@article{Roth2016,
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@phdthesis{Roth2017b,
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 year = {2017},
 title = {{Advanced~Kalman Filtering Approaches to Bayesian State Estimation}},
 address = {Link{\"o}ping, Sweden},
 school = {{Link{\"o}ping University}}
}


@electronic{Roth2017c,
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 year = {2017},
 title = {{Robust Bayesian Filtering and Smoothing Using Student's t Distribution}},
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@booklet{Ruckdeschel20,
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@book{Russell2000,
 author = {Russell, Evan and Chiang, Leo H. and Braatz, Richard D.},
 abstract = {{\dq}Early and accurate fault detection and diagnosis for modern chemical plants can minimize downtime, increase the safety of plant operations, and reduce manufacturing costs. The process monitoring techniques that have been most effective in practice are based on models constructed almost entirely from process data. The goal of the book is to present the theoretical background and practical techniques for data-driven process monitoring. Process monitoring techniques presented include: Principal component analysis ; Fisher discriminant analysis ; Partial least squares ; Canonical variate analysis. The text demonstrates the application of all of the data-driven process monitoring techniques to the Tennessee Eastman plant simulator, demonstrating the strengths and weaknesses of each approach in detail. This aids the reader in selecting the right method for his process application. Plant simulator and homework problems in which students apply the process monitoring techniques to a nontrivial simulated process, and can compare their performance with that obtained in the case studies in the text are included. A number of additional homework problems encourage the reader to implement and obtain a deeper understanding of the techniques. The reader will obtain a background in data-driven techniques for fault detection and diagnosis, including the ability to implement the techniques and to know how to select the right technique for a particular application{\dq}--Back cover.},
 year = {2000},
 title = {{Data-driven methods for fault detection and diagnosis in chemical processes}},
 address = {London and New York},
 publisher = {Springer},
 isbn = {9781852332587},
 series = {{Advances in industrial control}}
}


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