IJCNN International Joint Conference on Neural Networks: July 8-12, 1991, Washington State Convention & Trade Center, Seattle, WA, Volume 1IEEE, 1991 - Neural circuitry |
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Page 246
... pixels of ocean were extracted from a multispectral Landsat image . Half of these pixels were for use unchanged as nontarget pixels , i.e. , ocean . The other half were corrupted by the addition of a gray object ( target ) . This was ...
... pixels of ocean were extracted from a multispectral Landsat image . Half of these pixels were for use unchanged as nontarget pixels , i.e. , ocean . The other half were corrupted by the addition of a gray object ( target ) . This was ...
Page 247
... pixels representing each of the 8 materials . Note that when classifying noisy data having a standard deviation of 3 ... pixels actually belonging to the material represented by the row , which pixel is classified as the material ...
... pixels representing each of the 8 materials . Note that when classifying noisy data having a standard deviation of 3 ... pixels actually belonging to the material represented by the row , which pixel is classified as the material ...
Page 287
... pixels , variance number of Test Set applications % correct ( x 1,000 ) 93 50 learning rule GDR - 10.15 pixels , variance , mean 83 17 Slide ( 10 % ) pixels , variance , mean 82 2 GDR - 10.15 pixels , variance 77 16 Slide ( 10 % ) pixels ...
... pixels , variance number of Test Set applications % correct ( x 1,000 ) 93 50 learning rule GDR - 10.15 pixels , variance , mean 83 17 Slide ( 10 % ) pixels , variance , mean 82 2 GDR - 10.15 pixels , variance 77 16 Slide ( 10 % ) pixels ...
Contents
A Neurocomputational Approach | 11 |
Particle Tracking by Deformable Templates | 7 |
Comparison of Perceptron Training by Linear Programming and by the Perceptron Convergence | 8 |
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Common terms and phrases
accuracy algorithm analog annealing applications approach approximation architecture artificial neural network associated keywords backpropagation binary Boltzmann machine chip circuit classifier coefficients components compression computation convergence corresponding data set defined deformable templates detection detector diagnosis digits document dynamic thesaurus error estimate feature map feedforward Figure filter gradient gradient descent hidden layer hidden units Hough transform IEEE implementation input layer input pattern input vector Kohonen learning algorithm learning rule linear matrix measure method minimize multilayer perceptron neural net neurons noise nonlinear obtained operation optimal output layer output unit Parallel Distributed Processing parameters pattern recognition perceptron performance pi-sigma network pixels problem Projection Pursuit proposed recurrent neural networks sample segment self-organizing sensor shown sigmoid function signal simulated annealing simulation solution summing units techniques test set threshold tracks training set update values valve plate variables vector quantization weight vector