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Interpretable Piecewise Linear Classifier
http://hdl.handle.net/10445/5278
http://hdl.handle.net/10445/52789f7666e5-390d-4405-917e-526e47030008
| Item type | 会議発表論文 / Conference Paper(1) | |||||
|---|---|---|---|---|---|---|
| 公開日 | 2010-11-29 | |||||
| タイトル | ||||||
| タイトル | Interpretable Piecewise Linear Classifier | |||||
| 言語 | ||||||
| 言語 | eng | |||||
| 資源タイプ | ||||||
| 資源タイプ識別子 | http://purl.org/coar/resource_type/c_5794 | |||||
| 資源タイプ | conference paper | |||||
| アクセス権 | ||||||
| アクセス権 | metadata only access | |||||
| アクセス権URI | http://purl.org/coar/access_right/c_14cb | |||||
| 著者 |
Hartono, Pitoyo
× Hartono, Pitoyo |
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| 抄録 | ||||||
| 内容記述タイプ | Abstract | |||||
| 内容記述 | 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. | |||||
| 書誌情報 |
Proc. International Conference on Neural Information Processing (ICONIP 2007) p. 434-443, 発行日 2007 |
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| 査読有無 | ||||||
| 値 | あり/yes | |||||
| 研究業績種別 | ||||||
| 値 | 国際会議/International Conference | |||||
| 単著共著 | ||||||
| 値 | 単著/solo | |||||
| 出版者 | ||||||
| 出版者 | Springer LNCS 4985 | |||||
