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An Introduction to Computing with Neural Nets
An Introduction to Neural Computing
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activation Adaline adaptive ANNs applied architecture Artificial Intelligence associative memory backpropagation binary BoltzCONS Boltzmann machines cells classifier Cognitive Science competitive learning components Computer Conf connectionist connections constraints convergence convolution derivative described distributed representations dynamical elements encoding equations error example feedback Figure function GPFUs gradient descent Grossberg Hebbian Hebbian learning hidden units high-order Hinton Hopfield IEEE implemented input patterns input units input vector Kohonen layer learning algorithms learning procedure learning rule linear Machine Learning maps matrix minimize Neural Networks neurons node noise nonlinear optimal output units parallel Parallel Distributed Processing parameter pattern recognition perceptron performance prediction problem Proc propagation RABAM reinforcement learning represented retrieval Rumelhart Sejnowski self-organizing Self-Organizing Map sequence sigmoid signal simulation space speech recognition step stochastic structure subspace supervised learning supervised-learning Sutton TD methods teaching patterns Tech threshold tion top-down Touretzky training set unsupervised update variable weight vector Widrow zero