Learning under Non-Stationarity: Covariate Shift Adaptation, Class-Balance Change Adaptation, and Change Detection

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  • 非定常環境下での学習：共変量シフト適応，クラスバランス変化適応，変化検知
  • ヒテイジョウ カンキョウ カ デ ノ ガクシュウ : キョウ ヘンリョウ シフト テキオウ,クラスバランス ヘンカ テキオウ,ヘンカ ケンチ

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In standard supervised learning algorithms training and test data are assumed to follow the same probability distribution. However, because of a sample selection bias or non-stationarity of the environment, this important assumption is often violated in practice, which causes a significant estimation bias. In this article, we review semi-supervised adaptation techniques for coping with such distribution changes. We focus on two scenarios of such distribution change: the covariate shift (input distributions change but the input-output dependency does not change) and the class-balance change in classification (class-prior probabilities change but class-wise input distributions remain unchanged). We also show methods of change detection in probability distributions.

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