Please use this identifier to cite or link to this item: http://ktisis.cut.ac.cy/handle/10488/7145
Title: Classification of noisy patterns using ARTMAP-based neural networks
Authors: Kasparis, Takis 
Charalampidis, Dimitrios
Anagnostopoulos, Georgios C.
Keywords: Neural networks
Fuzzy sets
Pattern recognition
Classification
Issue Date: 2000
Publisher: SPIE
Source: Visual Information Processing IX, 2000, Orlando, Florida
Abstract: In this paper we present a modification of the test phase of ARTMAP-based neural networks that improves the classification performance of the networks when the patterns that are used for classification are extracted from noisy signals. The signals that are considered in this work are textured images, which are a case of 2D signals. Two neural networks from the ARTMAP family are examined, namely the Fuzzy ARTMAP (FAM) neural network and the Hypersphere ARTMAP (HAM) neural network. We compare the original FAM and HAM architectures with the modified ones, which we name FAM-m and HAM-m respectively. We also compare the classification performance of the modified networks, and of the original networks when they are trained with patterns extracted from noisy textures. Finally, we illustrate how combination of features can improve the classification performance for both the noiseless and noisy textures.
URI: http://ktisis.cut.ac.cy/handle/10488/7145
http://172.16.21.12:8080/jspui/handle/10488/7145
http://hdl.handle.net/10488/7145
ISSN: 0277786X
DOI: 10.1117/12.390470
Rights: © 2000 SPIE
Appears in Collections:Δημοσιεύσεις σε συνέδρια/Conference papers

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