Density Ratio Estimation in Machine Learning - Couverture rigide

Sugiyama, Masashi; Suzuki, Taiji; Kanamori, Takafumi

9780521190176: Density Ratio Estimation in Machine Learning

Couverture rigide

ISBN 10 : 0521190177 ISBN 13 : 9780521190176

Editeur : Cambridge University Press, 2012

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Machine learning is an interdisciplinary field of science and engineering that studies mathematical theories and practical applications of systems that learn. This book introduces theories, methods and applications of density ratio estimation, which is a newly emerging paradigm in the machine learning community. Various machine learning problems such as non-stationarity adaptation, outlier detection, dimensionality reduction, independent component analysis, clustering, classification and conditional density estimation can be systematically solved via the estimation of probability density ratios. The authors offer a comprehensive introduction of various density ratio estimators including methods via density estimation, moment matching, probabilistic classification, density fitting and density ratio fitting, as well as describing how these can be applied to machine learning. The book provides mathematical theories for density ratio estimation including parametric and non-parametric convergence analysis and numerical stability analysis to complete the first and definitive treatment of the entire framework of density ratio estimation in machine learning.

Les informations fournies dans la section � Synopsis � peuvent faire r�f�rence � une autre �dition de ce titre.

� propos des auteurs

Masashi Sugiyama is an Associate Professor in the Department of Computer Science at the Tokyo Institute of Technology.

Taiji Suzuki is an Assistant Professor in the Department of Mathematical Informatics at the University of Tokyo, Japan.

Takafumi Kanamori is an Associate Professor in the Department of Computer Science and Mathematical Informatics at Nagoya University, Japan.

Les informations fournies dans la section � A propos du livre � peuvent faire r�f�rence � une autre �dition de ce titre.

�diteurCambridge University Press
Date d'�dition2012
ISBN 10 0521190177
ISBN 13 9780521190176
ReliureReli�
Langueanglais
Nombre de pages342
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9781108461733: Density Ratio Estimation in Machine Learning

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Density Ratio Estimation in Machine Learning

Sugiyama, Masashi; Suzuki, Taiji; Kanamori, Takafumi

Edit� par Cambridge University Press, 2012

ISBN 10 : 0521190177 ISBN 13 : 9780521190176

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Density Ratio Estimation in Machine Learning

Sugiyama, Masashi; Suzuki, Taiji; Kanamori, Takafumi

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Density Ratio Estimation in Machine Learning

Sugiyama, Masashi|Suzuki, Taiji|Kanamori, Takafumi

Edit� par Cambridge University Press, 2012

ISBN 10 : 0521190177 ISBN 13 : 9780521190176

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Gebunden. Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Machine learning is an interdisciplinary field of science and engineering that studies mathematical theories and practical applications of systems that learn. The book introduces theories, methods and applications of density ratio estimation. This is the fi. N� de r�f. du vendeur 446929409

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Density Ratio Estimation in Machine Learning

Masashi Sugiyama

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ISBN 10 : 0521190177 ISBN 13 : 9780521190176

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Density Ratio Estimation in Machine Learning

Sugiyama, Masashi/ Suzuki, taiji/ Kanamori, Takafumi

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Hardcover. Etat : Brand New. 1st edition. 344 pages. 9.00x6.25x1.00 inches. In Stock. This item is printed on demand. N� de r�f. du vendeur __0521190177

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Density Ratio Estimation in Machine Learning

Takafumi Kanamori Taiji Suzuki Masashi Sugiyama

Edit� par Cambridge University Press CUP, 2012

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Etat : New. pp. 342. N� de r�f. du vendeur 2614401948

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Density Ratio Estimation in Machine Learning (Hardcover)

Masashi Sugiyama

Edit� par Cambridge University Press, Cambridge, 2012

ISBN 10 : 0521190177 ISBN 13 : 9780521190176

Neuf Couverture rigide

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Hardcover. Etat : new. Hardcover. Machine learning is an interdisciplinary field of science and engineering that studies mathematical theories and practical applications of systems that learn. This book introduces theories, methods and applications of density ratio estimation, which is a newly emerging paradigm in the machine learning community. Various machine learning problems such as non-stationarity adaptation, outlier detection, dimensionality reduction, independent component analysis, clustering, classification and conditional density estimation can be systematically solved via the estimation of probability density ratios. The authors offer a comprehensive introduction of various density ratio estimators including methods via density estimation, moment matching, probabilistic classification, density fitting and density ratio fitting, as well as describing how these can be applied to machine learning. The book provides mathematical theories for density ratio estimation including parametric and non-parametric convergence analysis and numerical stability analysis to complete the first and definitive treatment of the entire framework of density ratio estimation in machine learning. Machine learning is an interdisciplinary field of science and engineering that studies mathematical theories and practical applications of systems that learn. The book introduces theories, methods and applications of density ratio estimation. This is the first and definitive treatment of the entire framework of density ratio estimation. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. N� de r�f. du vendeur 9780521190176

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