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• W2114054925 abstract "In this paper we introduce free energy-based methods to constrain mutual information maximization, developed to realize competitive learning. The new method is introduced to simplify the computational procedures of mutual information and to improve the fidelity of representation and to stabilize learning. First, the free energy is effective in simplifying the computation procedures of mutual information because we need not directly compute mutual information, which needs heavy computation, but only deals with partition functions. With partition functions, computational complexity is significantly reduced. Second, fidelity to input patterns can be improved because training errors between input patterns and connection weights are implicitly incorporated. This means that mutual information is maximized under the constraint of the errors between input patterns and connection weights. Finally, learning can be stabilized in our approach. One of the problems of the free energy approach is that learning processes..." @default.
• W2114054925 created "2016-06-24" @default.
• W2114054925 creator A5079603834 @default.
• W2114054925 date "2011-10-01" @default.
• W2114054925 modified "2023-09-23" @default.
• W2114054925 title "Constrained information maximization by free energy minimization" @default.
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• W2114054925 doi "https://doi.org/10.1080/03081079.2010.549486" @default.
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