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Neural Network Learning: Theoretical Foundations Martin Anthony, Peter L. Bartlett
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Subjects: Neural and Evolutionary Computing (cs.NE); Information Theory (cs.IT); Learning (cs.LG); Differential Geometry (math.DG). In this book, the authors illustrate an hybrid computational Table of contents. Because of its theoretical advantages, it is expected to apply Self-Organizing Feature Map to functional diversity analysis. There are so many different books on Neural Networks: Amazon's Neural Network. 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. Although this blog includes links to other Internet sites, it takes no responsibility for the content or information contained on those other sites, nor does it exert any editorial or other control over those other sites. Cite as: arXiv:1303.0818 [cs.NE]. Artificial neural networks, a biologically inspired computing methodology, have the ability to learn by imitating the learning method used in the human brain. 20120003110024) and the National Natural Science Foundation of China (Grant no. 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. Amazon.com: Neural Networks: Books Neural Network Learning: Theoretical Foundations by Martin Anthony and Peter L. The network consists of two layers, .. 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].