Dataset shift in machine learning / / [edited by] Joaquin Quiñonero-Candela [and others]

• Record Nr.: UNINA9910782857003321
• Pubbl/distr/stampa: Cambridge, Mass., : MIT Press, ©2009
• ISBN: 0-262-29253-X; 1-282-24038-2; 0-262-25510-3
• Descrizione fisica: 1 online resource (246 p.)
• Collana: Neural information processing series
• Altri autori (Persone): Quiñonero-CandelaJoaquin
• Disciplina: 006.3/1
• Soggetti: Machine learning
• 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.