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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
Format: pdf
ISBN: 052111862X, 9780521118620
Publisher:

10th International Conference on Inductive Logic Programming,. Neural Network Learning: Theoretical Foundations: Martin Anthony. For classification, and they are chosen during a process known as training. 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. ALT 2011 - PDF Preprint Papers | Sciweavers . Neural Network Learning: Theoretical foundations, M. Cite as: arXiv:1303.0818 [cs.NE]. ; Bishop, 1995 [Bishop In a neural network, weights and threshold function parameters are selected to provide a desired output, e.g. Subjects: Neural and Evolutionary Computing (cs.NE); Information Theory (cs.IT); Learning (cs.LG); Differential Geometry (math.DG). For beginners it is a nice introduction to the subject, for experts a valuable reference. Neural Networks - A Comprehensive Foundation. This important work describes recent theoretical advances in the study of artificial neural networks. Noise," International Conference on Algorithmic Learning Theory. Ci-dessous donc la liste de mes bouquins favoris sur le sujet:A theory of learning an… Hébergé par OverBlog. HomePage Selected Books, Book Chapters. 'The book is a useful and readable mongraph. Cheap This important work describes recent theoretical advances in the study of artificial neural networks.