MBNR: Case-Based Reasoning with Local Feature Weighting by Neural Network

Issue Title: Special Issue: Soft Computing in Case Based Reasoning Our aim is to build an integrated learning framework of neural network and case-based reasoning. The main idea is that feature weights for case-based reasoning can be evaluated by neural networks. In this paper, we propose MBNR (Memo...

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Veröffentlicht in:Applied intelligence (Dordrecht, Netherlands) Netherlands), 2004-11, Vol.21 (3), p.265-276
Hauptverfasser: Park, Jae Heon, Im, Kwang Hyuk, Shin, Chung-Kwan, Park, Sang Chan
Format: Artikel
Sprache:eng
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Zusammenfassung:Issue Title: Special Issue: Soft Computing in Case Based Reasoning Our aim is to build an integrated learning framework of neural network and case-based reasoning. The main idea is that feature weights for case-based reasoning can be evaluated by neural networks. In this paper, we propose MBNR (Memory-Based Neural Reasoning), case-based reasoning with local feature weighting by neural network. In our method, the neural network guides the case-based reasoning by providing case-specific weights to the learning process. We developed a learning algorithm to train the neural network to learn the case-specific local weighting patterns for case-based reasoning. We showed the performance of our learning system using four datasets.[PUBLICATION ABSTRACT]
ISSN:0924-669X
1573-7497
DOI:10.1023/B:APIN.0000043559.83167.3d