Dataset Shift in Machine Learning

Quinonero-candela, Joaquin (Editor)/ Sugiyama, Masashi (Editor)/ Schwaighofer, Anton (Editor)/ Lawrence, Neil D. (Editor)

ISBN 10: 026254587X / ISBN 13: 9780262545877

Edité par Mit Pr, 2022

A propos de cet article

248 pages. 10.00x8.00x0.56 inches. In Stock.

N° de réf. du vendeur x-026254587X

Détails bibliographiques

• Titre: Dataset Shift in Machine Learning
• Éditeur: Mit Pr
• Date de parution: 2022
• Langue: anglais
• ISBN à 10 chiffres: 026254587X
• ISBN à 13 chiffres: 9780262545877
• Reliure: Paperback
• État de l'article: Brand New

A propos de ce titre

Synopsis

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
 

À propos de l?auteur

Joaquin Quiñonero-Candela
Joaquin Quiñonero-Candela is a Researcher in the Online Services and Advertising Group at Microsoft Research Cambridge, U.K.
 
Masashi Sugiyama
Masashi Sugiyama is Director of the RIKEN Center for Advanced Intelligence Project and Professor of Computer Science at the University of Tokyo.
 
Anton Schwaighofer
Anton Schwaighofer is an Applied Researcher in the Online Services and Advertising Group at Microsoft Research, Cambridge, U.K.
 
Neil D. Lawrence
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.