Jump to ContentJump to Main Navigation
Dataset Shift in Machine Learning$
Users without a subscription are not able to see the full content.

Joaquin Quiñonero-Candela, Masashi Sugiyama, Anton Schwaighofer, and Neil D. Lawrence

Print publication date: 2008

Print ISBN-13: 9780262170055

Published to MIT Press Scholarship Online: August 2013

DOI: 10.7551/mitpress/9780262170055.001.0001

Show Summary Details
Page of

PRINTED FROM MIT PRESS SCHOLARSHIP ONLINE (www.mitpress.universitypressscholarship.com). (c) Copyright The MIT Press, 2021. All Rights Reserved. An individual user may print out a PDF of a single chapter of a monograph in MITSO for personal use.date: 23 September 2021

Discriminative Learning under Covariate Shift with a Single Optimization Problem

Discriminative Learning under Covariate Shift with a Single Optimization Problem

Chapter:
(p.161) 9 Discriminative Learning under Covariate Shift with a Single Optimization Problem
Source:
Dataset Shift in Machine Learning
Author(s):

Bickel Amir

Brückner Michael

Scheffer Tobias

Publisher:
The MIT Press
DOI:10.7551/mitpress/9780262170055.003.0009

This chapter derives a discriminative model for learning under differing training and test distributions, and is organized as follows. Section 9.2 formalizes the problem setting. Section 9.3 reviews models for different training and test distributions. Section 9.4 introduces the discriminative model, and Section 9.5 describes the joint optimization problem. Primal and kernelized classifiers are derived for various training and test distributions in Sections 9.6 and 9.7. Section 9.8 analyzes the convexity of the integrated optimization problem. Section 9.9 provides empirical results, and Section 9.10 concludes.

Keywords:   covariate shift adaptation, training, test distributions, discriminative model, joint optimization, kernelize learning

MIT Press Scholarship Online requires a subscription or purchase to access the full text of books within the service. Public users can however freely search the site and view the abstracts and keywords for each book and chapter.

Please, subscribe or login to access full text content.

If you think you should have access to this title, please contact your librarian.

To troubleshoot, please check our FAQs, and if you can't find the answer there, please contact us.