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Neural Network Learning: Theoretical Foundations Martin Anthony, Peter L. Bartlett
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The network consists of two layers, .. 10th International Conference on Inductive Logic Programming,. 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]. This important work describes recent theoretical advances in the study of artificial neural networks. For beginners it is a nice introduction to the subject, for experts a valuable reference. 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. 20120003110024) and the National Natural Science Foundation of China (Grant no. Because of its theoretical advantages, it is expected to apply Self-Organizing Feature Map to functional diversity analysis. Neural Network Learning: Theoretical Foundations: Martin Anthony. 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. 'The book is a useful and readable mongraph. In this book, the authors illustrate an hybrid computational Table of contents.