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MBNR: Case-Based Reasoning with Local Feature Weighting by Neural Network.
- Source :
- Applied Intelligence; Nov/Dec2004, Vol. 21 Issue 3, p265-276, 12p, 4 Black and White Photographs, 2 Diagrams, 5 Charts, 7 Graphs
- Publication Year :
- 2004
-
Abstract
- 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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 0924669X
- Volume :
- 21
- Issue :
- 3
- Database :
- Complementary Index
- Journal :
- Applied Intelligence
- Publication Type :
- Academic Journal
- Accession number :
- 15835577
- Full Text :
- https://doi.org/10.1023/B:APIN.0000043559.83167.3d