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Neural Network Learning: Theoretical Foundations Martin Anthony, Peter L. Bartlett
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Cheap This important work describes recent theoretical advances in the study of artificial neural networks. 10th International Conference on Inductive Logic Programming,. Neural Networks - A Comprehensive Foundation. Artificial Neural Networks Mathematical foundations of neural networks. Learning theory (supervised/ unsupervised/ reinforcement learning) Knowledge based networks. ; Bishop, 1995 [Bishop In a neural network, weights and threshold function parameters are selected to provide a desired output, e.g. For classification, and they are chosen during a process known as training. 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. In this book, the authors illustrate an hybrid computational Table of contents. 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. 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.