Classification Of Noisy Patterns Using Artmap-Based Neural Networks
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.
Proceedings of SPIE - The International Society for Optical Engineering
Number of Pages
Article; Proceedings Paper
Source API URL
Charalampidis, Dimitrios; Anagnostopoulos, Georgios; and Kasparis, Takis, "Classification Of Noisy Patterns Using Artmap-Based Neural Networks" (2000). Scopus Export 2000s. 1277.