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Neural Network Learning: Theoretical Foundations Martin Anthony, Peter L. Bartlett
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Product DescriptionThis important work describes recent theoretical advances in the study of artificial neural networks. Cheap This important work describes recent theoretical advances in the study of artificial neural networks. Share this I'm a bit of a freak – enterprise software team lead during the day and neural network researcher during the evening. ; Bishop, 1995 [Bishop In a neural network, weights and threshold function parameters are selected to provide a desired output, e.g. My guess is that these patterns will not only be useful for machine learning, but also any other computational work that involves either a) processing large amounts of data, or b) algorithms that take a significant amount of time to execute. 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. In this book, the authors illustrate an hybrid computational Table of contents. 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. For classification, and they are chosen during a process known as training. This important work describes recent theoretical advances in the study of artificial neural networks. 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. Ci-dessous donc la liste de mes bouquins favoris sur le sujet:A theory of learning an… Hébergé par OverBlog. Neural Networks - A Comprehensive Foundation. Neural Network Learning: Theoretical foundations, M.