Fuzzy Art And Fuzzy Artmap With Adaptively Weighted Distances
Adaptive resonance theory; Classification; Fuzzy ART; Fuzzy ARTMAP
In this paper, we introduce a modification of the Fuzzy ARTMAP (FAM) neural network, namely, the Fuzzy ARTMAP with adaptively weighted distances (FAMawd) neural network. In FAMawd we substitute the regular L1-norm with a weighted L1-norm to measure the distances between categories and input patterns. The distance-related weights are a function of a category's shape and allow for bias in the direction of a category's expansion during learning. Moreover, the modification to the distance measurement is proposed in order to study the capability of FAMawd in achieving more compact knowledge representation than FAM, while simultaneously maintaining good classification performance. For a special parameter setting FAMawd simplifies to the original FAM, thus, making FAMawd a generalization of the FAM architecture. We also present an experimental comparison between FAMawd and FAM on two benchmark classification problems in terms of generalization performance and utilization of categories. Our obtained results illustrate FAMawd's potential to exhibit low memory utilization, while maintaining classification performance comparable to FAM. © Society of Photo-Optical Instrumentation Engineers.
Proceedings of SPIE - The International Society for Optical Engineering
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Charalampidis, Dimitrios; Anagnostopoulos, Georgios C.; and Georgiopoulos, Michael, "Fuzzy Art And Fuzzy Artmap With Adaptively Weighted Distances" (2002). Scopus Export 2000s. 2927.