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001 008494
005 20170928113026.0
008 130809s1994####xxua#####b####000#0#eng#d
020 ## $a 0133341860
082 04 $a 006.3
100 1# $a Fausett, Laurene.
245 10 $a Fundamentals of neural networks : $b architectures, algorithms, and applications / $c Laurene Fausett.
260 ## $a Englewood Cliffs, NJ : $b Prentice-Hall, $c c1994.
300 ## $a xvi, 461 p. : $b il. ; $c 23 cm.
504 ## $a Incluye referencias bibliográficas (p. 437-447) e índice.
505 0# $a Ch. 1. Introduction -- 1.1. Why Neural Networks and Why Now? -- 1.2. What Is a Neural Net? -- 1.3. Where Are Neural Nets Being Used? -- 1.4. How Are Neural Networks Used? -- 1.5. Who Is Developing Neural Networks? -- 1.6. When Neural Nets Began: the McCulloch-Pitts Neuron -- Ch. 2. Simple Neural Nets for Pattern Classification -- 2.1. General Discussion -- 2.2. Hebb Net -- 2.3. Perceptron -- 2.4. Adaline -- Ch. 3. Pattern Association -- 3.1. Training Algorithms for Pattern Association -- 3.2. Heteroassociative Memory Neural Network -- 3.3. Autoassociative Net -- 3.4. Iterative Autoassociative Net -- 3.5. Bidirectional Associative Memory (BAM) -- Ch. 4. Neural Networks Based on Competition -- 4.1. Fixed-Weight Competitive Nets -- 4.2. Kohonen Self-Organizing Maps -- 4.3. Learning Vector Quantization -- 4.4. Counterpropagation -- Ch. 5. Adaptive Resonance Theory -- 5.1. Introduction -- 5.2. Art1 -- 5.3. Art2 -- Ch. 6. Backpropagation Neural Net -- 6.1. Standard Backpropagation -- 6.2. Variations -- 6.3. Theoretical Results -- Ch. 7. A Sampler of Other Neural Nets -- 7.1. Fixed Weight Nets for Constrained Optimization -- 7.2. A Few More Nets that Learn -- 7.3. Adaptive Architectures -- 7.4. Neocognitron.
653 ## $a Redes neuronales (Computación)

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