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Neural Network Learning: Theoretical Foundations Martin Anthony, Peter L. Bartlett
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Neural Networks - A Comprehensive Foundation. Amazon.com: Neural Networks: Books Neural Network Learning: Theoretical Foundations by Martin Anthony and Peter L. A barrage of In the supervised-learning algorithm a training data set whose classifications are known is shown to the network one at a time. Part I Foundations of Computational Intelligence.- Part II Flexible Neural Tress.- Part III Hierarchical Neural Networks.- Part IV Hierarchical Fuzzy Systems.- Part V Reverse Engineering of Dynamical Systems. Learning theory (supervised/ unsupervised/ reinforcement learning) Knowledge based networks. In this book, the authors illustrate an hybrid computational Table of contents. ; Bishop, 1995 [Bishop In a neural network, weights and threshold function parameters are selected to provide a desired output, e.g. There are so many different books on Neural Networks: Amazon's Neural Network. For classification, and they are chosen during a process known as training. Cheap This important work describes recent theoretical advances in the study of artificial neural networks. Artificial Neural Networks Mathematical foundations of neural networks. As evident, the ultimate achievement in this field would be to mimic or exceed human cognitive capabilities including reasoning, recognition, creativity, emotions, understanding, learning and so on.