TY  - BOOK
AU  - Sugiyama,Masashi
AU  - Suzuki,Taiji
AU  - Kanamori,Takafumi
TI  - Density ratio estimation in machine learning
SN  - 9781139035613 (ebook)
AV  - QA276.8 .S84 2012
U1  - 006.3/1 23
PY  - 2012///
CY  - Cambridge
PB  - Cambridge University Press
KW  - Estimation theory
KW  - Machine learning
N1  - Title from publisher's bibliographic system (viewed on 05 Oct 2015); Density estimation -- Moment matching -- Probabilistic classification -- Density fitting -- Density-ratio fitting -- Unified framework -- Direct density-ratio estimation with dimensionality reduction -- Importance sampling -- Distribution comparison -- Mutual information estimation -- Conditional probability estimation -- Parametric convergence analysis -- Non-parametric convergence analysis -- Parametric two-sample test -- Non-parametric numerical stability analysis -- Conclusions and future directions
N2  - 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
UR  - https://doi.org/10.1017/CBO9781139035613
ER  -