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.

Author: Masashi Sugiyama, Taiji Suzuki, Takafumi Kanamori
Publisher: Cambridge University Press
Published: 02/20/2012
Pages: 342
Binding Type: Hardcover
Weight: 1.32lbs
Size: 9.20h x 6.20w x 0.90d
ISBN13: 9780521190176
ISBN10: 0521190177
BISAC Categories:
- Computers | Artificial Intelligence | Computer Vision & Pattern Recognit