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

Neural Network Learning: Theoretical Foundations



Neural Network Learning: Theoretical Foundations book download




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


For classification, and they are chosen during a process known as training. 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. Neural Network Learning: Theoretical foundations, M. Neural Network Learning: Theoretical Foundations: Martin Anthony. 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. Neural Networks - A Comprehensive Foundation. Subjects: Neural and Evolutionary Computing (cs.NE); Information Theory (cs.IT); Learning (cs.LG); Differential Geometry (math.DG). Share this I'm a bit of a freak – enterprise software team lead during the day and neural network researcher during the evening. 10th International Conference on Inductive Logic Programming,. 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. This important work describes recent theoretical advances in the study of artificial neural networks. Ci-dessous donc la liste de mes bouquins favoris sur le sujet:A theory of learning an… Hébergé par OverBlog.