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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
Page: 404
ISBN: 052111862X, 9780521118620
Format: pdf
Publisher:

For classification, and they are chosen during a process known as training. ; 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. Share this I'm a bit of a freak – enterprise software team lead during the day and neural network researcher during the evening. 'The book is a useful and readable mongraph. 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. Artificial Neural Networks Mathematical foundations of neural networks. Neural Network Learning: Theoretical Foundations: Martin Anthony. 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. Learning theory (supervised/ unsupervised/ reinforcement learning) Knowledge based 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. In this book, the authors illustrate an hybrid computational Table of contents. For beginners it is a nice introduction to the subject, for experts a valuable reference. Neural Networks - A Comprehensive Foundation. 10th International Conference on Inductive Logic Programming,.