Please use this identifier to cite or link to this item: http://ktisis.cut.ac.cy/handle/10488/7119
Title: Fuzzy ART and fuzzy ARTMAP with adaptively weighted distances
Authors: Kasparis, Takis 
Charalampidis, Dimitrios
Anagnostopoulos, Georgios C.
Keywords: Classification
Computer architecture
Neural networks
Issue Date: 2002
Publisher: IEEE
Source: Applications and Science Computational Intelligence V, 2002, Orlando, Florida
Abstract: n 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.
URI: http://ktisis.cut.ac.cy/handle/10488/7119
http://172.16.21.12:8080/jspui/handle/10488/7119
http://hdl.handle.net/10488/7119
ISSN: 0277786X
DOI: 10.1117/12.458723
Rights: © 2002 IEEE
Appears in Collections:Δημοσιεύσεις σε συνέδρια/Conference papers

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