title: Supervised adaptive Hamming net for classification of multiple-valued patterns.
abstract: 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.
authors.affiliation: Department of Control Engineering, National Chiao Tung University, Hsinchu, Taiwan, R.O.C.
authors.forename: C A
authors.initials: CA
authors.lastname: Hung
authors.forename: S F
authors.initials: SF
authors.lastname: Lin
mesh_terms: D001185:Artificial Intelligence; D001245:Association Learning; D003198:Computer Simulation; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated