Generalization Ability of Hopfield Neural Network with Spin-S Ising Neurons
Katsuki Katayama, Tsuyoshi Horiguchi · Journal of the Physical Society of Japan · 2000
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.