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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:

Neural Networks - A Comprehensive Foundation. Learning theory (supervised/ unsupervised/ reinforcement learning) Knowledge based networks. Some titles of books I've been reading in the past two weeks: M. Bartlett — Neural Network Learning: Theoretical Foundations; M. Biggs — Computational Learning Theory; L. 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. Artificial Neural Networks Mathematical foundations of neural networks. Subjects: Neural and Evolutionary Computing (cs.NE); Information Theory (cs.IT); Learning (cs.LG); Differential Geometry (math.DG). At the end of the day it was decided that to wrap up all the discussions and move forward into designing the “Internet of Education” conference in 2013 as the yearly flagship conference of Knowledge 4 All Foundation Ltd. ; Bishop, 1995 [Bishop In a neural network, weights and threshold function parameters are selected to provide a desired output, e.g. Cite as: arXiv:1303.0818 [cs.NE]. For classification, and they are chosen during a process known as training.