Introduction To The Theory Of Neural ComputationAvalon Publishing, 24 черв. 1991 р. - 327 стор. Comprehensive introduction to the neural network models currently under intensive study for computational applications. It also provides coverage of neural network applications in a variety of problems of both theoretical and practical interest. |
Зміст
TWO The Hopfield Model | 11 |
THREE Extensions of the Hopfield Model | 43 |
FOUR Optimization Problems | 71 |
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Introduction To The Theory Of Neural Computation John A. Hertz,Anders S. Krogh,Richard G. Palmer Обмежений попередній перегляд - 2018 |
Introduction To The Theory Of Neural Computation, Volume I John A Hertz,Anders Krogh,Richard G Palmer Перегляд фрагмента - 1991 |
Загальні терміни та фрази
algorithm applied approach appropriate architecture attractor average back-propagation binary bits Boltzmann machine calculate Chapter competitive learning computation connection strengths context units continuous-valued convergence cost function defined discussed dynamics eigenvalues eigenvector energy function equations equilibrium error example feature mapping feed-forward feed-forward networks FIGURE finite given gives gradient descent Hebb rule Hebbian learning hidden layer hidden units Hopfield network implementation input patterns input space input vector Kohonen learning rule linear linearly magnetic matrix mean field minimize neural networks neurons nonlinear Oja's optimization output layer output units parameters particular perceptron Perror possible principal component probability problem random receptive fields recurrent network result Santa Fe Institute Section sequence shown in Fig shows signal simple perceptron solution solved spin stable statistical mechanics stochastic stochastic network subspace symmetric temperature term training set unsupervised learning update values weight space weight vector wij's zero