Park, Jae Heon; Im, Kwang Hyuk; Shin, Chung-Kwan; Park, Sang Chan MBNR: Case-based reasoning with local feature weighting by neural network. (English) Zbl 1101.68781 Appl. Intell. 21, No. 3, 265-276 (2004). Summary: 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. MSC: 68T05 Learning and adaptive systems in artificial intelligence Keywords:local feature weighting; case-based reasoning; neural network; hybrid system PDFBibTeX XMLCite \textit{J. H. Park} et al., Appl. Intell. 21, No. 3, 265--276 (2004; Zbl 1101.68781) Full Text: DOI