Machine learning in non-stationary environments : introduction to covariate shift adaptation /

This volume focuses on a specific non-stationary environment known as covariate shift, in which the distributions of inputs (queries) changes but the conditional distributions of outputs (answers) is unchanged, and presents machine learning theory algorithms, and applications to overcome this variet...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Sugiyama, Masashi, 1974-
Otros Autores: Kawanabe, Motoaki
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Cambridge, Mass. : MIT Press, ©2012.
Colección:Adaptive computation and machine learning.
Temas:
Machine learning.
Apprentissage automatique.
COMPUTERS > Enterprise Applications > Business Intelligence Tools.
COMPUTERS > Intelligence (AI) & Semantics.
COMPUTERS > Machine Theory.
Machine learning
COMPUTER SCIENCE/Machine Learning & Neural Networks
COMPUTER SCIENCE/General
COMPUTER SCIENCE/Artificial Intelligence
Acceso en línea:Texto completo
Descripción
Sumario:This volume focuses on a specific non-stationary environment known as covariate shift, in which the distributions of inputs (queries) changes but the conditional distributions of outputs (answers) is unchanged, and presents machine learning theory algorithms, and applications to overcome this variety of non-stationarity.
Descripción Física:1 online resource (xiv, 261 pages) : illustrations
Bibliografía:Includes bibliographical references and index.
ISBN:9780262301220
0262301229
1280499222
9781280499227

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