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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
Publisher:
ISBN: 052111862X, 9780521118620

Ярлыки: tutorials djvu ebook hotfile epub chm filesonic rapidshare Tags:Neural Network Learning: Theoretical Foundations fileserve pdf downloads torrent book. Because of its theoretical advantages, it is expected to apply Self-Organizing Feature Map to functional diversity analysis. 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. Опубликовано 31st May пользователем Vadym Garbuzov. 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. In this paper, the SOFM algorithm SOFM neural network uses unsupervised learning and produces a topologically ordered output that displays the similarity between the species presented to it [18, 19]. Neural Networks - A Comprehensive Foundation. Artificial neural networks, a biologically inspired computing methodology, have the ability to learn by imitating the learning method used in the human brain. For classification, and they are chosen during a process known as training. Subjects: Neural and Evolutionary Computing (cs.NE); Information Theory (cs.IT); Learning (cs.LG); Differential Geometry (math.DG). Product DescriptionThis important work describes recent theoretical advances in the study of artificial neural networks. 20120003110024) and the National Natural Science Foundation of China (Grant no. The network consists of two layers, ..