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ART1: Similarity Measures

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Abstract

This paper concerns with the ART1 (Adaptive Resonance Theory 1) in Neural Network. Important features of ART1 are similarity measure (criterion), vigilance parameter (ρ), and their function to classify the input patterns. Experimental results show that the similarity measure as designed originally does not increase the number of categories with the increased value of ρ but decreases, too. This is against the claim of ‘stability-plasticity’ dilemma. A number of researchers have considered this and suggested alternative similarity measures. Here, we propose a new similarity criterion which eliminates this problem and also possesses the property of lowest list presentations needed for self stabilization of the network. We compare the results of different similarity criteria experimentally and present them in graphs. Analysis of the network under noisy environment is also carried out.

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References

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Authors and Affiliations

  1. Computer Science and Information Management Program, Asian Institute of Technology, GPO Box 2754, Bangkok, 10501, Thailand

    Ei Ei Khin & R. Sadananda

  2. Dept. of Information Technology, Sirindhorn International Institute of Technology, Thammasat University, P.O. Box 22, Thammasat Rangsit P.O., Pathumthani, 12121, Thailand

    Amarottam Shrestha

Authors
  1. Ei Ei Khin
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  2. Amarottam Shrestha
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  3. R. Sadananda
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Khin, E.E., Shrestha, A. & Sadananda, R. ART1: Similarity Measures. Neural Processing Letters 6, 109–117 (1997). https://doi.org/10.1023/A:1009619908131

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  • DOI: https://doi.org/10.1023/A:1009619908131

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