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- W2099306150 abstract "In this paper, we propose a D-entropy minimisation method, aiming to unify information maximization and minimization methods. The D-entropy is defined by difference between Renyi's entropy and Shannon's entropy. The D-entropy minimization corresponds to both mutual information maximization and minimization. Thus, the method can be used to interpret explicitly internal representations and to improve generalization. The D-entropy minimization was applied to two problems: six alphabet character recognition and the inference of well-formedness of artificial strings. Experimental results confirmed that by minimizing the D-entropy a small number of principal hidden units can be detected and generalization performance can be improved." @default.
- W2099306150 created "2016-06-24" @default.
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- W2099306150 date "2002-11-22" @default.
- W2099306150 modified "2023-09-26" @default.
- W2099306150 title "D-entropy minimization: integration of mutual information maximization and minimization" @default.
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- W2099306150 doi "https://doi.org/10.1109/icnn.1997.616174" @default.
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