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Neural Network Learning: Theoretical Foundations Martin Anthony, Peter L. Bartlett
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Product DescriptionThis important work describes recent theoretical advances in the study of artificial neural networks. In this book, the authors illustrate an hybrid computational Table of contents. ALT 2011 - PDF Preprint Papers | Sciweavers . Artificial Neural Networks Mathematical foundations of neural networks. HomePage Selected Books, Book Chapters. 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. For beginners it is a nice introduction to the subject, for experts a valuable reference. Noise," International Conference on Algorithmic Learning Theory. Subjects: Neural and Evolutionary Computing (cs.NE); Information Theory (cs.IT); Learning (cs.LG); Differential Geometry (math.DG). My guess is that these patterns will not only be useful for machine learning, but also any other computational work that involves either a) processing large amounts of data, or b) algorithms that take a significant amount of time to execute. Share this I'm a bit of a freak – enterprise software team lead during the day and neural network researcher during the evening. Cite as: arXiv:1303.0818 [cs.NE]. 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. There are so many different books on Neural Networks: Amazon's Neural Network. 'The book is a useful and readable mongraph. The artificial neural networks, which represent the electrical analogue of the biological nervous systems, are gaining importance for their increasing applications in supervised (parametric) learning problems. Amazon.com: Neural Networks: Books Neural Network Learning: Theoretical Foundations by Martin Anthony and Peter L. Learning theory (supervised/ unsupervised/ reinforcement learning) Knowledge based networks.