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portada Density Ratio Estimation in Machine Learning
Type
Physical Book
Publisher
Author
Year
2012
Language
English
Pages
342
Format
Hardcover
ISBN
0521190177
ISBN13
9780521190176
Edition No.
1
Categories

Density Ratio Estimation in Machine Learning

Sugiyama; Suzuki; Kanamori (Author) · Cambridge University Press · Hardcover

Density Ratio Estimation in Machine Learning - Sugiyama; Suzuki; Kanamori

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Synopsis "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. 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 also 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.

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