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Please use this identifier to cite or link to this item: http://hdl.handle.net/10445/5278

Title: Interpretable Piecewise Linear Classifier
Authors: Hartono, Pitoyo
Abstract: 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.
Research Achievement Classification: 国際会議/International Conference
Type: Conference Paper
Peer Review: あり/yes
Solo/Joint Author(s): 単著/solo
Published journal or presented
academic conference: 
Proc. International Conference on Neural Information Processing (ICONIP 2007)
Spage: 434
Epage: 443
Date: 2007
Publisher: Springer LNCS 4985
Appears in Collections:Pitoyo, Hartono

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