標題: Supervised adaptive hamming net for classification of multiple-valued patterns
作者: Hung, CA
Lin, SF
電控工程研究所
Institute of Electrical and Control Engineering
公開日期: 1-四月-1997
摘要: A Supervised Adaptive Hamming Net (SAHN) is introduced for incremental learning of recognition categories in response to arbitrary sequences of multiple-valued or binary-valued input patterns. The binary-valued SAHN derived from the Adaptive Hamming Net (AHN) is functionally equivalent to a simplified ARTMAP, which is specifically designed to establish many-to-one mappings. The generalization to learning multiple-valued input patterns is achieved by incorporating multiple-valued logic into the AHN. In this paper, we examine some useful properties of learning in a P-valued SAHN. In particular, an upper bound is derived on the number of epochs required by the P-valued SAHN to learn a list of input-output pairs that is repeatedly presented to the architecture. Furthermore, we connect the P-valued SAHN with the binary-valued SAHN via the thermometer code.
URI: http://hdl.handle.net/11536/613
ISSN: 0129-0657
期刊: INTERNATIONAL JOURNAL OF NEURAL SYSTEMS
Volume: 8
Issue: 2
起始頁: 181
結束頁: 200
顯示於類別:期刊論文