Density Ratio Estimation in Machine Learning

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Masashi Sugiyama (Tokyo Institute of Technology) Taiji Suzuki (University of Tokyo)

Density Ratio Estimation in Machine Learning

Masashi Sugiyama (Tokyo Institute of Technology), Taiji Suzuki (University of Tokyo), Takafumi Kanamori (Nagoya University, Japan)

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English

Cambridge University Press
20 February 2012

Machine learning; Pattern recognition

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.

By: Masashi Sugiyama (Tokyo Institute of Technology), Taiji Suzuki (University of Tokyo), Takafumi Kanamori (Nagoya University, Japan)
Imprint: Cambridge University Press
Country of Publication: United Kingdom
Dimensions: Height: 234mm, Width: 157mm, Spine: 23mm
Weight: 600g
ISBN: 9780521190176
ISBN 10: 0521190177
Pages: 342
Publication Date: 20 February 2012
Audience: Professional and scholarly , Undergraduate
Format: Hardback
Publisher's Status: Active

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.

Reviews for Density Ratio Estimation in Machine Learning

'There is no doubt that this book will change the way people think about machine learning and stimulate many new directions for research.' Thomas G. Dietterich, from the Foreword There is no doubt that this book will change the way people think about machine learning and stimulate many new directions for research. From the Foreword by Thomas G. Dietterich The book is well written and produced, and will probably be seen in retrospect as a significant addition to the literature in this important area--at least to the extent that density ratio estimation as a technique proves useful in real-world applications. Future work and applications using the theory presented should indicate to what extent this happens. Shrisha Rao, Computing Reviews This book is clear and well written, and it is an excellent introduction to density ratio estimation in both theory and practice. It presents the state-of-the-art methodology on this topic and in this regard it is really nice that the bibliography is so exhaustive and well commented. Pierre Alquir, Mathematical Reviews