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このアイテムの引用には次の識別子を使用してください: http://hdl.handle.net/10445/5278

タイトル: Interpretable Piecewise Linear Classifier
著者: Hartono, Pitoyo
アブストラクト: The objective of this study is to build a model of neural network classifier that is not only reliable but also, as opposed to most presently available neural networks, logically interpretable in a human-plausible manner. Presently, most of the studies of rule extraction from trained neural networks focus on extracting rule from existing neural network models that were designed without the consideration of rule extraction, hence after the training process they are meant to be used as a kind black box. Consequently, this makes rule extraction a hard task. In this study we construct a model of neural network ensemble with the consideration of rule extraction. The function of the ensemble can be easily interpreted to generate logical rules that are understandable to human. We believe that the interpretability of neural networks contributes to the improvement of the reliability and the usability of neural networks when applied critical real world problems.
研究業績種別: 国際会議/International Conference
資料種別: Conference Paper
査読有無: あり/yes
単著共著: 単著/solo
開始ページ: 434
終了ページ: 443
年月日: 2007年
出版社: Springer LNCS 4985
出現コレクション:ピトヨ・ハルトノ

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