DocumentCode :
2855913
Title :
Neural α-feature detector for feature detection and generalization
Author :
Kamimura, Ryotaro
Author_Institution :
Inf. Sci. Lab., Tokai Univ., Kanagawa, Japan
Volume :
3
fYear :
1998
fDate :
4-9 May 1998
Firstpage :
1845
Abstract :
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
Keywords :
entropy; feature extraction; feedforward neural nets; minimisation; α-entropy; Renyi entropy; Shannon entropy; consonant cluster formation; information minimisation; input patterns; neural α-feature detector; Computer vision; Data mining; Detectors; Entropy; Feedforward systems; Information processing; Information science; Laboratories; Random variables; Uncertainty;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks Proceedings, 1998. IEEE World Congress on Computational Intelligence. The 1998 IEEE International Joint Conference on
Conference_Location :
Anchorage, AK
ISSN :
1098-7576
Print_ISBN :
0-7803-4859-1
Type :
conf
DOI :
10.1109/IJCNN.1998.687138
Filename :
687138
Link To Document :
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