1.

Record Nr.

UNINA9910954803903321

Titolo

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

Pubbl/distr/stampa

Cambridge, Mass., : MIT Press, c2009

ISBN

9780262292535

026229253X

9781282240384

1282240382

9780262255103

0262255103

Edizione

[1st ed.]

Descrizione fisica

1 online resource (246 p.)

Collana

Neural information processing series

Altri autori (Persone)

Quinonero-CandelaJoaquin

Disciplina

006.3/1

Soggetti

Machine learning

Machine learning - Mathematical models

Lingua di pubblicazione

Inglese

Formato

Materiale a stampa

Livello bibliografico

Monografia

Note generali

Description based upon print version of record.

Nota di bibliografia

Includes bibliographical references and index.

Nota di contenuto

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

Sommario/riassunto

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.