Comparative Performance Measures of Fuzzy ARTMAP, Learned Vector Quantization, and Back Propagation for Handwritten Character Recognition
| Data(s) |
14/11/2011
14/11/2011
01/02/1992
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| Resumo |
This article compares the performance of Fuzzy ARTMAP with that of Learned Vector Quantization and Back Propagation on a handwritten character recognition task. Training with Fuzzy ARTMAP to a fixed criterion used many fewer epochs. Voting with Fuzzy ARTMAP yielded the highest recognition rates. British Petroleum (89-A-1204); Defense Advanced Research Projects Agency (90-0083); National Science Foundation (IRI 90-00530); Office of Naval Research (N00014-91-J-4100); Air Force Office of Scientific Research (90-0175); Sharp Corporation, Information System Research & Development Center, Nara, Japan |
| Identificador | |
| Idioma(s) |
en_US |
| Publicador |
Boston University Center for Adaptive Systems and Department of Cognitive and Neural Systems |
| Relação |
BU CAS/CNS Technical Reports;CAS/CNS-TR-1992-005 |
| Direitos |
Copyright 1992 Boston University. Permission to copy without fee all or part of this material is granted provided that: 1. The copies are not made or distributed for direct commercial advantage; 2. the report title, author, document number, and release date appear, and notice is given that copying is by permission of BOSTON UNIVERSITY TRUSTEES. To copy otherwise, or to republish, requires a fee and / or special permission. Boston University Trustees |
| Tipo |
Technical Report |
