9780262170055

Dataset Shift in Machine Learning

by ; ; ;
  • ISBN13:

    9780262170055

  • ISBN10:

    0262170051

  • Format: Hardcover
  • Copyright: 2009-02-27
  • Publisher: Mit Pr
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Summary

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 Bruckner, David Corfield, Amir Globerson, Arthur Gretton, Lars Kai Hansen, Matthias Hein, Jiayuan Huang, Choon Hui Teo, Takafumi Kanamori, Klaus-Robert Muller, Sam Roweis, Neil Rubens, Tobias Scheffer, Marcel Schmittfull, Bernhard Scholkopf Hidetoshi Shimodaira, Alex Smola, Amos Storkey, Masashi Sugiyama Neural Information Processing series

Table of Contents

Introduction to dataset shiftp. 1
When training and test sets are different: characterizing learning transferp. 3
Projection and projectabilityp. 29
Theoretical views on dataset and covariate shiftp. 39
Binary classification under sample selection biasp. 41
On Bayesian transduction: implications for the covariate shift problemp. 65
On the training/test distributions gap: a data representation learning frameworkp. 73
Algorithms for covariate shiftp. 85
Geometry of covariate shift with applications to active learningp. 87
A conditional expectation approach to model selection and active learning under covariate shiftp. 107
Covariate shift by kernel mean matchingp. 131
Discriminative learning under covariate shift with a single optimization problemp. 161
An adversarial view of covariate shift and a minimax approachp. 179
Discussionp. 199
Author commentsp. 201
Referencesp. 207
Notation and symbolsp. 219
Contributorsp. 223
Indexp. 227
Table of Contents provided by Blackwell. All Rights Reserved.

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