種々の分類器を対象として，有限個の学習データのみが利用可能である現実においても理論的に的確で計算量的にも実際的な，分類器性能評価手法を提案する．分類器評価における難しさは，有限データのみの利用に起因する分類誤り推定に伴う偏りの発生にある．この困難を解決するため，「境界曖昧性」と呼ばれる新しい評価尺度を提案し，それを用いる評価法の有用性を，3種の分類器と13個のデータセットを用いた実験を通して実証する． / We propose a general method that makes accurate evaluation of any classifier model for realistic tasks, both in a theoretical sense despite the finiteness of the available data, and in a practical sense in terms of computation costs. The classifier evaluation challenge arises from the bias of the classification error estimate that is only based on finite data. We bypass this existing difficulty by proposing a new classifier evaluation measure called "boundary uncertainty'' whose estimate based on finite data can be considered a reliable representative of its expectation based on infinite data, and demonstrate the potential of our approach on three classifier models and thirteen datasets. / 博士(工学) / Doctor of Philosophy in Engineering / 同志社大学 / Doshisha University