Applications of Artificial Neural Networks II: 2-5 April 1991, Orlando, Florida, Part 1 |
Common terms and phrases
accuracy application approach architecture Artificial Neural Networks attractors back-propagation basis functions binary cluster complex connectionist connections convergence correlation corresponding data set decision tree detector sets discrimination dynamics equation error example feature extraction feature space feature vectors filter Gabor Gabor filter Gaussian gradient descent hidden layer Hopfield IEEE implementation interconnections iterations k-nearest neighbor LCTV learning algorithm linear Mahalanobis distance mapping mathematical morphology matrix method minimization module multilayer perceptron netlist neural net neuron nodes noise nonlinear number of neurons object operation optical optical correlator optimally pruned subtree oriented output layer parallel parameters pattern recognition performance pixel plane problem processor prototype quantization represents rules of selecting samples segmentation selecting cells shown in Figure sigmoid signal simulation solution spatial spatial light modulators structure supervised learning T₁ target techniques template testing SNR training data training set transformation update vision weights WENet