Dataset Shift in Machine Learning

Erscheinungsjahr 2008
ISBN: 978-0-262-17005-5
Verlag: MIT PR

Buch, Englisch, 245 Seiten, Format (B × H): 207 mm x 261 mm, Gewicht: 704 g

Reihe: Neural Information Processing

An overview of recent efforts in the machine learning community to deal with dataset and covariate shift, which occurs when test and training inputs and outputs have different distributions.Dataset shift is a common problem in predictive modeling that occurs when the joint distribution of inputs and outputs differs between training and test stages. Covariate shift, a particular case of dataset shift, occurs when only the input distribution changes. Dataset shift is present in most practical applications, for reasons ranging from the bias introduced by experimental design to the irreproducibility of the testing conditions at training time. (An example is -email spam filtering, which may fail to recognize spam that differs in form from the spam the automatic filter has been built on.) Despite this, and despite the attention given to the apparently similar problems of semi-supervised learning and active learning, dataset shift has received relatively little attention in the machine learning community until recently. This volume offers an overview of current efforts to deal with dataset and covariate shift. The chapters offer a mathematical and philosophical introduction to the problem, place dataset shift in relationship to transfer learning, transduction, local learning, active learning, and semi-supervised learning, provide theoretical views of dataset and covariate shift (including decision theoretic and Bayesian perspectives), and present algorithms for covariate shift.Contributors
Shai Ben-David, Steffen Bickel, Karsten Borgwardt, Michael Brückner, David Corfield, Amir Globerson, Arthur Gretton, Lars Kai Hansen, Matthias Hein, Jiayuan Huang, Choon Hui Teo, Takafumi Kanamori, Klaus-Robert Müller, Sam Roweis, Neil Rubens, Tobias Scheffer, Marcel Schmittfull, Bernhard Schölkopf Hidetoshi Shimodaira, Alex Smola, Amos Storkey, Masashi Sugiyama

Autoren/Hrsg.

Quiñonero-Candela, Joaquin

Sugiyama, Masashi

Schwaighofer, Anton

Lawrence, Neil D.

Weitere Mitwirkende

Storkey, Amos

Corfield, David

Hein, Matthias

Hansen, Lars Kai

Ben-David, Shai

Kanamori, Takafumi

Shimodaira, Hidetoshi

Sugiyama, Masashi

Rubens, Neil

Müller, Klaus-Robert

Gretton, Arthur

Smola, Alexander J.

Huang, Jiayuan

Schmittfull, Marcel

Borgwardt, Karsten

Schölkopf, Bernhard

Bickel, Steffen

Brückner, Michael

Scheffer, Tobias

Globerson, Amir

Teo, Choon Hui

Roweis, Sam

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Automatische Datenerfassung, Datenanalyse

Über Autor(innen)

Sugiyama, Masashi
Masashi Sugiyama is Associate Professor in the Department of Computer Science at Tokyo Institute of Technology.

Quiñonero-Candela, Joaquin
Joaquin Quiñ onero-Candela is a Researcher in the Online Services and Advertising Group at Microsoft Research Cambridge, U.K.

Sugiyama, Masashi
Masashi Sugiyama is Associate Professor in the Department of Computer Science at Tokyo Institute of Technology.

Müller, Klaus-Robert
Klaus-Robert Müller is Head of the Intelligent Data Analysis group at the Fraunhofer Institute and Professor in the Department of Computer Science at the Technical University of Berlin.

Lawrence, Neil D.
Neil D. Lawrence is Senior Lecturer and Member of the Machine Learning and Optimisation Research Group in the School of Computer Science at the University of Manchester.

Schwaighofer, Anton
Anton Schwaighofer is an Applied Researcher in the Online Services and Advertising Group at Microsoft Research, Cambridge, U.K.

Smola, Alexander J.
Alexander J. Smola is Senior Principal Researcher and Machine Learning Program Leader at National ICT Australia/Australian National University, Canberra.

Schölkopf, Bernhard
Bernhard Schölkopf is Director at the Max Planck Institute for Intelligent Systems in Tübingen, Germany. He is coauthor of Learning with Kernels (2002) and is a coeditor of Advances in Kernel Methods: Support Vector Learning (1998), Advances in Large-Margin Classifiers (2000), and Kernel Methods in Computational Biology (2004), all published by the MIT Press.

Joaquin Quiñonero-Candela is a Researcher in the Online Services and Advertising Group at Microsoft Research Cambridge, U.K. Masashi Sugiyama is Associate Professor in the Department of Computer Science at the Tokyo Institute of Technology. Anton Schwaighofer is an Applied Researcherin the Online Services and Advertising Group at Microsoft Research, Cambridge, U.K. Neil D. Lawrence is Senior Research Fellow and Member of the Machine Learning and Optimisation Research Group in the School of Computer Science at the University of Manchester.