Neural Network Learning: Theoretical Foundations by Martin Anthony, Peter L. Bartlett

Neural Network Learning: Theoretical Foundations Martin Anthony, Peter L. Bartlett ebook
Publisher:
Page: 404
Format: pdf
ISBN: 052111862X, 9780521118620

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. 20120003110024) and the National Natural Science Foundation of China (Grant no. 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. The network consists of two layers, .. Cheap This important work describes recent theoretical advances in the study of artificial neural networks. Because of its theoretical advantages, it is expected to apply Self-Organizing Feature Map to functional diversity analysis. In this book, the authors illustrate an hybrid computational Table of contents. 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. 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. ; Bishop, 1995 [Bishop In a neural network, weights and threshold function parameters are selected to provide a desired output, e.g.