Dataset shift in machine learning / [edited by] Joaquin Quinonero-Candela ... [et al.].

Format:

Book

Contributor:

Quiñonero-Candela, Joaquin.

Series:

Neural information processing series.

Neural information processing series

Language:

English

Subjects (All):

Machine learning.

Machine learning--Mathematical models.

Physical Description:

1 online resource (246 p.)

Edition:

1st ed.

Place of Publication:

Cambridge, Mass. : MIT Press, c2009.

Language Note:

English

Summary:

This work is 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.

Contents:

Contents; Series Foreword; Preface; I - Introduction to Dataset Shift; 1 - When Training and Test Sets Are Different: Characterizing Learning Transfer; 2 - Projection and Projectability; II - Theoretical Views on Dataset and Covariate Shift; 3 - Binary Classi cation under Sample Selection Bias; 4 - On Bayesian Transduction: Implications for the Covariate Shift Problem; 5 - On the Training/Test Distributions Gap: A Data Representation Learning Framework; III - Algorithms for Covariate Shift; 6 - Geometry of Covariate Shift with Applications to Active Learning

7 - A Conditional Expectation Approach to Model Selection and Active Learning under Covariate Shift 8 - Covariate Shift by Kernel Mean Matching; 9 - Discriminative Learning under Covariate Shift with a Single Optimization Problem; 10 - An Adversarial View of Covariate Shift and a Minimax Approach; IV - Discussion; 11 - Author Comments; References; Notation and Symbols; Contributors; Index

Notes:

Description based upon print version of record.

Includes bibliographical references and index.

Made available online by Ebrary.

OCLC-licensed vendor bibliographic record.

ISBN:

9780262292535

026229253X

9781282240384

1282240382

9780262255103

0262255103

OCLC:

310915974

Publisher Number:

9786612240386