IEEE ... International Conference on Neural Networks, Volumes 1-4Shun'ichi Amari SOS Printing, 2000 - Artificial intelligence |
Contents
Erkki Oja Helsinki University of Technology Finland | 1 |
Josef Goppert Wolfgang Rosenstiel University of Tubingen Germany | 7 |
Roberto Horowitz Luis Alvarez University of California at Berkeley | 13 |
Copyright | |
94 other sections not shown
Other editions - View all
Common terms and phrases
adaptation application approach architecture artificial neural networks backpropagation classification clusters components connection criterion data set defined denotes density estimation dimensional distribution dynamic epochs equation example fault tolerance feedforward Figure function approximation Gaussian given gradient Hebbian Hebbian learning hidden nodes hidden units hyperplane IEEE IEEE Trans input space input vector iteration kernel Kohonen learning algorithm learning rate linear matrix mean square error method minimal multilayer multilayer perceptrons neighborhood neuron noise nonlinear number of hidden obtained optimal output layer overfitting parameters patterns perceptron performance prediction error problem Proc proposed Q-learning quantization radial basis function RBFN reconstruction error recursive robust samples self-organizing map sensors shown shows sigmoidal function signal simulation solution sparse stochastic structure supervised learning synapse technique Theorem topology training algorithm training data training set University unsupervised update weight decay weight vector width