Density Ratio Estimation in Machine Learning: Sugiyama, Masashi, Suzuki, Taiji, Kanamori, Takafumi

This book is a groundbreaking work in the field of machine learning, introducing a new paradigm for density ratio estimation that can solve various problems such as non-stationarity adaptation, outlier detection, dimensionality reduction, independent component analysis, clustering, classification, and conditional density estimation.

The authors provide a comprehensive introduction to various density ratio estimators, including methods via density estimation, moment matching, probabilistic classification, density fitting, and density ratio fitting, and describe how these can be applied to machine learning.

The book also includes mathematical theories for density ratio estimation, such as parametric and non-parametric convergence analysis and numerical stability analysis.

With its well-written and produced content, this book is sure to change the way people think about machine learning and stimulate many new directions for research.

It is a must-read for anyone interested in this important area of study.