Statistical learning theory understands learning as a function estimation problem on empirical data. The goal are generalizing modells which are able to make predictions for new and unseen data points. Conditions on those generalizations are analyzed, e.g. a finite VC-dimension. This finally leads to risk bounds for classification algoithms.
A major idea coming from statistical learning theory is the idea of structural risk minimization. This idea motivates new classification algorithms like Support Vector Machines / Relevance Vector Machines which provide better risk bounds.
Mierswa/Morik/2008a |
Mierswa, Ingo and Morik, Katharina.
About the Non-Convex Optimization Problem Induced by Non-positive Semidefinite Kernel Learning.
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pages 241--258,
2008.
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Mierswa/2007a |
Mierswa, Ingo.
Controlling Overfitting with Multi-Objective Support Vector Machines.
In
Proc. of the Genetic and Evolutionary Computation Conference (GECCO 2007; best paper award),
2007.
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Mierswa/2007c |
Mierswa, Ingo.
Regularization through Multi-Objective Optimization.
In
Klinkenberg, Ralf and Mierswa, Ingo and Hinneburg, Alexander and Posch, Stefan and Neumann, Steffen (editors),
Proc. of LWA 2007 - Lernen - Wissensentdeckung - Adaptivität,
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Mierswa/2006a |
Mierswa, Ingo.
Evolutionary Learning with Kernels: A Generic Solution for Large Margin Problems.
In
Proc. of the Genetic and Evolutionary Computation Conference (GECCO 2006),
2006.
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Mierswa/2006b |
Mierswa, Ingo.
Making Indefinite Kernel Learning Practical.
Collaborative Research Center 475, University of Dortmund,
2006.
|
Morik/Rueping/2006a |
Morik, Katharina and Rüping, Stefan.
Klassifikations-/Clustermethoden und Konjunkturanalyse.
Duncker & Humbolt,
2006.
|
Morik/Rueping/2006b |
Morik, Katharina and Rüping, Stefan.
An Inductive Logic Programming Approach to the Classification of Phases in Business Cycles.
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Heilemann, U. and Weihs, Claus (editors),
Classification and Clustering in Business Cycle Analysis,
Duncker & Humbolt,
2006.
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Scholz/2006a |
Scholz, Martin.
Boosting in PN Spaces.
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Johannes Furnkranz, Tobias Scheffer, Myra Spiliopoulou (editors),
Proceedings of the 17th European Conference on Machine Learning (ECML-06),
Vol. 4212,
pages 377--388,
Berlin, Germany,
Springer,
2006.
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Rueping/Scheffer/2005a |
Ruping, Stefan and Scheffer, Tobias (editors).
Proceedings of the ICML 2005 Workshop on Learning with Multiple Views.
2005.
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Rueping/2004a |
Rüping, Stefan.
A Simple Method for Estimating Conditional Probabilities in SVMs.
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Abecker, A. and Bickel, S. and Brefeld, U. and Drost, I. and Henze,N. and Herden, O. and Minor, M. and Scheffer, T. and Stojanovic, L. and Weibelzahl, S. (editors),
Lernen - Wissensentdeckung - Adaptivität,
Berlin,
Humboldt-Universität,
2004.
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Rueping/2004b |
Rüping, Stefan.
Probabilistic SVMs - How Much Scaling Do We Need?.
2004.
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Rueping/Morik/2003a |
Rüping, Stefan and Morik, Katharina.
Support Vector Machines and Learning about Time.
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2003.
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Rueping/Morik/2003b |
Rüping, Stefan and Morik, Katharina.
Support Vector Machines and Learning about Time.
No. 4,
SFB475, Universitat Dortmund,
Dortmund, Germany,
2003.
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Morik/etal/2002a |
Morik, Katharina and Joachims, T. and Imhoff, M. and Brockhausen, P. and Rüping, S..
Integrating Kernel Methods into a Knowledge-based Approach to Evidence-based Medicine.
In
Schmitt, Manfred and Teodorescu, Horia-Nicolai and Jain, Ashlesha and Jain, Ajita and Jain, Sandhya and Jain, Lakhmi C. (editors),
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Vol. 96,
pages 71--99,
Physica-Verlag,
2002.
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Morik/Rueping/2002a |
Morik, Katharina and Rüping, Stefan.
A Multistrategy Approach to the Classification of Phases in Business Cycles.
In
Elomaa, Taprio and Mannila, Heikki and Toivonen, Hannu (editors),
Machine Learning: ECML 2002,
Vol. 2430,
pages 307--318,
Berlin,
Springer,
2002.
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Rueping/2002a |
Stefan Rüping.
Support Vector Machines in Relational Databases.
In
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Pattern Recognition with Support Vector Machines --- First International Workshop, SVM 2002,
pages 310--320,
Springer,
2002.
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Rueping/2002b |
Rüping, Stefan.
Efficient Kernel Calculation for Multirelational Data.
In
Kokai, Gabriella and Zeidler, Jens (editors),
Proceedings der FGML 2002,
pages 121-126,
Learning Lab Lower Saxony, Hannover, Germany,
2002.
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Rueping/2002c |
Rüping, Stefan.
Incremental Learning with Support Vector Machines.
No. 18,
SFB475, Universitat Dortmund,
Dortmund, Germany,
2002.
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Rueping/2001a |
Rüping, Stefan.
SVM Kernels for Time Series Analysis.
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LLWA 01 - Tagungsband der GI-Workshop-Woche Lernen - Lehren - Wissen - Adaptivitat,
pages 43-50,
Dortmund, Germany,
2001.
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Rueping/2001b |
Rüping, Stefan.
Incremental Learning with Support Vector Machines.
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Proceedings of the 2001 IEEE International Conference on Data Mining (ICDM '01),
pages 641--642,
IEEE,
2001.
|
Morik/etal/99a |
Morik, Katharina and Brockhausen, Peter and Joachims, Thorsten.
Combining statistical learning with a knowledge-based approach -- A case study in intensive care monitoring.
In
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pages 268--277,
San Francisco, CA, USA,
Morgan Kaufmann Publishers Inc.,
1999.
|
Rueping/99a |
Rüping, Stefan.
Zeitreihenprognose fur Warenwirtschaftssysteme unter Berucksichtigung asymmetrischer Kostenfunktionen.
Universitat Dortmund,
1999.
|
Schewe/97b |
Schewe, Sandra.
Automatische Kategorisierung von Volltexten unter Anwendung von NLP-Techniken.
Fachbereich Informatik, Universitat Dortmund,
1997.
|
Wermeckes/96a |
Wermeckes, Thorsten.
Erweiterung von COBWEB/CLASSIST zur Behandlung von zeitveranderlichen Daten.
Fachbereich Informatik, Universitat Dortmund,
1996.
|
Robers/95a |
Ursula Robers.
Entwicklung eines wissensbasierten Assistentensystems zur Analyse von Fall--Kontroll--Studien.
Universitat Dortmund, Fachbereich Informatik, Lehrstuhl VIII,
1995.
|
Wessel/95a |
Stefanie Wessel.
Lernen qualitativer Merkmale aus numerischen Robotersensordaten.
Fachbereich Informatik, Universitat Dortmund,
1995.
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