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Neural α-feature detector for feature detection and generalization

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1 Author(s)
Kamimura, R. ; Inf. Sci. Lab., Tokai Univ., Kanagawa, Japan

We propose a neural α-feature detector used to extract a small number of main or essential features in input patterns. Features can be detected by controlling α-entropy for α-feature detectors. The α-entropy is defined by the difference between Renyi entropy and Shannon entropy. The α-entropy controller aims to maximize information contained in a few important α-feature detectors, while information for all other feature detectors is minimized. Thus, the α-entropy controller can maximize and simultaneously minimize information. The neural α-feature detector was applied to the inference of consonant cluster formation. Experimental results confirmed that by controlling α-entropy a small number of principal features can be detected, which can intuitively be interpreted. In addition, we could see that generalization performance is improved by minimizing α-entropy

Published in:

Neural Networks Proceedings, 1998. IEEE World Congress on Computational Intelligence. The 1998 IEEE International Joint Conference on  (Volume:3 )

Date of Conference:

4-9 May 1998