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Selective Information Acquisition with Application to Pattern Classification
Como, Italy July 24-July 27
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/IJCNN.2000.857837IEEE-INNS-ENNS International Joint Co ...
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Ryotaro Kamimura, Tokai University
In this paper, we pr p se a selective information acquisition device which can be used to store information selectively according to the important or characteristics of input patterns. For selective information acquisition, we introduce _-information to distort the ordinary Shannon information function. We have no choice but to maximize or minimize information to eliminate this distortion. Thus, the distortion elimination can be employed as a basic mechanism of the device to maximize or minimize information selectively. The information device is applied to a phonological feature detection problem. In this problem, experimental results confirmed that conditional information is flexibly maximized or minimized, depending upon input patterns. We could also see that conditional information is a good measure to distinguish between different classes, and that the strength of conditional information is used to classify input patterns.
Citation:
Ryotaro Kamimura, "Selective Information Acquisition with Application to Pattern Classification," ijcnn, vol. 1, pp.1203, IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 1, 2000
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