Please use this identifier to cite or link to this item:
https://hdl.handle.net/20.500.14279/2502
Title: | Classification of noisy patterns using ARTMAP-based neural networks | Authors: | Kasparis, Takis Charalampidis, Dimitrios Anagnostopoulos, Georgios C. |
metadata.dc.contributor.other: | Κασπαρής, Τάκης | Major Field of Science: | Engineering and Technology | Field Category: | Electrical Engineering - Electronic Engineering - Information Engineering | Keywords: | Neural networks;Fuzzy sets;Pattern recognition;Classification | Issue Date: | 29-Jun-2000 | Source: | Visual Information Processing IX, 2000, Orlando, Florida | Conference: | SPIE Conference Proceedings | 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. | ISSN: | 0277-786X | DOI: | 10.1117/12.390470 | Rights: | © 2000 SPIE | Type: | Conference Papers | Affiliation: | University of Central Florida | Affiliation : | University of Central Florida | Publication Type: | Peer Reviewed |
Appears in Collections: | Δημοσιεύσεις σε συνέδρια /Conference papers or poster or presentation |
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