Titlebook: Neural Networks; A Systematic Introdu Raúl Rojas Textbook 1996 Springer-Verlag Berlin Heidelberg 1996 Spline.artificial intelligence.fuzzy

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,Weighted Networks—The Perceptron,n and of simulating any finite automaton. From the biological point of view, however, the types of network that can be built are not very relevant. The computing units are too similar to conventional logic gates and the network must be completely specified before it can be used. There are no free pa

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Perceptron Learning,meters adequate for a given task was left open. If two sets of points have to be separated linearly with a perceptron, adequate weights for the computing unit must be found. The operators that we used in the preceding chapter, for example for edge detection, used hand customized weights. Now we woul

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Unsupervised Learning and Clustering Algorithms,eacher is needed to accept or reject the output and adjust the network weights if necessary. Some researchers have proposed alternative learning methods in which the network parameters are determined as a result of a self-organizing process. In . corrections to the network weights are not performed

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The Backpropagation Algorithm, computing units. However the computational effort needed for finding the correct combination of weights increases substantially when more parameters and more complicated topologies are considered. In this chapter we discuss a popular learning method capable of handling such large learning problems—

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Fast Learning Algorithms,arning problems were developed. After the pioneering work of Rosenblatt and others, no efficient learning algorithm for multilayer or arbitrary feed forward neural networks was known. This led some to the premature conclusion that the whole field had reached a dead-end. The rediscovery of the backpr

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