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Neural Network Learning: Theoretical Foundations by Martin Anthony, Peter L. Bartlett

Neural Network Learning: Theoretical Foundations Martin Anthony, Peter L. Bartlett ebook
Publisher:
Format: pdf
ISBN: 052111862X, 9780521118620
Page: 404

Neural Networks - A Comprehensive Foundation. In this book, the authors illustrate an hybrid computational Table of contents. Subjects: Neural and Evolutionary Computing (cs.NE); Information Theory (cs.IT); Learning (cs.LG); Differential Geometry (math.DG). 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. Cheap This important work describes recent theoretical advances in the study of artificial neural networks. 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. ; Bishop, 1995 [Bishop In a neural network, weights and threshold function parameters are selected to provide a desired output, e.g. Share this I'm a bit of a freak – enterprise software team lead during the day and neural network researcher during the evening. 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. Cite as: arXiv:1303.0818 [cs.NE]. The artificial neural networks, which represent the electrical analogue of the biological nervous systems, are gaining importance for their increasing applications in supervised (parametric) learning problems. 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. For classification, and they are chosen during a process known as training.