Contents Introduction Learning processes Single layer perception Mutilayer perception Radial Basis function networks Support vector machines Committee machines Principal Components analysis Self organizing maps Information Theoretic models Stochastic machines and their approximates rooted in statistical mechanics Neurodynamic Programming Temporal Processing using Feedforward networks Neurodynamics Dynamically driven recurrent networks
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