Document Detail

A neural network classifier with disjunctive fuzzy information.
MedLine Citation:
PMID:  12662779     Owner:  NLM     Status:  Publisher    
This paper presents a neural network classifier that learns disjunctive fuzzy information in the feature space. This neural network consists of two types of nodes in the hidden layer. The prototype nodes and exemplar nodes represent cluster centroids and exceptions in the feature space, respectively. This classifier automatically generates and refines prototypes for distinct clusters in the feature space. The number and sizes of these prototypes are not restricted, so the prototypes will form near-optimal decision regions to meet the distribution of input patterns and classify as many input patterns as possible. Next, exemplars will be created and expanded to learn the patterns that cannot be classified by the prototypes. Such a training strategy can reduce the memory requirement and speed up the process of non-linear classification. In addition, on-line learning is supplied in this classifier and the computational load is lightened. The experimental results manifest that this model can reduce the number of hidden nodes by determining the appropriate number of prototype nodes.
Hahn Ming Lee; Kuo Hsiu Chen; I Feng Jiang
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Publication Detail:
Journal Detail:
Title:  Neural networks : the official journal of the International Neural Network Society     Volume:  11     ISSN:  1879-2782     ISO Abbreviation:  Neural Netw     Publication Date:  1998 Aug 
Date Detail:
Created Date:  2003-Mar-28     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  8805018     Medline TA:  Neural Netw     Country:  -    
Other Details:
Languages:  ENG     Pagination:  1113-1125     Citation Subset:  -    
Department of Electronic Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan
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