This paper reviews a new framework for statistical machine learning that we introduced recently. A distinctive feature of this framework is that various machine learning problems are formulated as a problem of estimating the ratio of probability densities in a unified way. Then the density ratio is estimated without going through the hard task of density estimation, which results in accurate estimation. This density ratio framework includes various machine learning tasks such as non-stationarity adaptation, outlier detection, dimensionality reduction, independent component analysis, and conditional density estimation. Thus, density ratio estimation is a highly versatile tool for machine learning.%本論文では，我々が最近導入した統計的機械学習の新しい枠組みを紹介する．この枠組みの 特徴は，様々な機械学習問題を確率密度関数の比の推定問題に帰着させるところにある．そし て，困難な確率密度関数の推定を経由せずに，確率密度比を直接推定することにより，精度良 く学習を行う．この密度比推定の枠組みには，非定常環境適応，異常値検出，次元削減，独立 成分分析，因果推定，条件付き確率推定など様々な機械学習の問題が含まれるため，極めて汎 用的である．
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