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Neural Network Learning: Theoretical Foundations Martin Anthony, Peter L. Bartlett
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For beginners it is a nice introduction to the subject, for experts a valuable reference. This important work describes recent theoretical advances in the study of artificial neural networks. Amazon.com: Neural Networks: Books Neural Network Learning: Theoretical Foundations by Martin Anthony and Peter L. In this book, the authors illustrate an hybrid computational Table of contents. 'The book is a useful and readable mongraph. There are so many different books on Neural Networks: Amazon's Neural Network. 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]. The network consists of two layers, .. 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. 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. 20120003110024) and the National Natural Science Foundation of China (Grant no. HomePage Selected Books, Book Chapters. ALT 2011 - PDF Preprint Papers | Sciweavers . Noise," International Conference on Algorithmic Learning Theory. Because of its theoretical advantages, it is expected to apply Self-Organizing Feature Map to functional diversity analysis.