Generalization Ability of Hopfield Neural Network with Spin-S Ising Neurons : General Physics
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概要
- 論文の詳細を見る
We investigate a fully connected Hopfield neural network with spin-S (S <_-1) Ising neurons, including S = ∞, when binary patterns are embedded by the Hebbian learning rule. We analyze the energy function of the neural network using the replica method. We investigate a generalization ability of the neural network within the replica symmetric (RS) solutions. We clarify. that the generalization ability of the neural network with a larger value of S is enhanced when the finite number of concepts are extracted from the presented examples.
- 社団法人日本物理学会の論文
- 2000-09-15
著者
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Katayama Katsuki
Department Of Computer And Mathematical Sciences Graduate School Of Information Sciences Tohoku Univ
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Katayama Katsuki
Department Of Computer And Mathematical Sciences Graduate School Of Information Sciences Tohoku Univ
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Horiguchi Tsuyoshi
Department Of Computer And Mathematical Sciences Graduate School Of Information Sciences Tohoku Univ
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Horiguchi Tsuyoshi
Department Of Applied Science Faculty Of Engineering Tohoku University
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