Density Ratio Estimation in Machine Learning
Masashi Sugiyama author Taiji Suzuki author Takafumi Kanamori author
Format:Hardback
Publisher:Cambridge University Press
Published:20th Feb '12
Currently unavailable, and unfortunately no date known when it will be back
This hardback is available in another edition too:
- Paperback£37.99(9781108461733)
This book introduces theories, methods and applications of density ratio estimation, a newly emerging paradigm in the machine learning community.
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.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.
'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
ISBN: 9780521190176
Dimensions: 234mm x 157mm x 23mm
Weight: 600g
342 pages