Density Ratio Estimation in Machine Learning

By: Masashi Sugiyama, Taiji Suzuki, Takafumi Kanamori

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Hardcover | 20 February 2012

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Hardcover
342 Pages

Dimensions(cm)
23.39 x 15.6 x 2.06

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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.

Industry Reviews

'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

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You Can Find This Book In

Non-Fiction Computing & I.T.Computer Science Artificial Intelligence Computer Vision Machine Learning Pattern Recognition

Density Ratio Approach to Machine Learning:
Introduction
Methods of Density Ratio Estimation:
Density estimation
Moment matching
Probabilistic classification
Density fitting
Density-ratio fitting
Unified framework
Direct density-ratio estimation with dimensionality reduction
Applications of Density Ratios in Machine Learning:
Importance sampling
Distribution comparison
Mutual information estimation
Conditional probability estimation
Theoretical Analysis of Density Ratio Estimation:
Parametric convergence analysis
Non-parametric convergence analysis
Parametric two-sample test
Non-parametric numerical stability analysis
Conclusions:
Conclusions and future directions
Table of Contents provided by Publisher. All Rights Reserved.

Density Ratio Estimation in Machine Learning

At a Glance

Hardcover

Industry Reviews

Shipping

How to return your order

Defective items

You Can Find This Book In

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