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

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

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. 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. Underlying this need is the concept of “ connectionism”, which is concerned with the computational and learning capabilities of assemblies of simple processors, called artificial neural networks. Neural Network Learning: Theoretical foundations, M. Share this I'm a bit of a freak – enterprise software team lead during the day and neural network researcher during the evening. Ci-dessous donc la liste de mes bouquins favoris sur le sujet:A theory of learning an… Hébergé par OverBlog. 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. Download free Neural Networks and Computational Complexity (Progress in Theoretical Computer Science) H. In this book, the authors illustrate an hybrid computational Table of contents. HomePage Selected Books, Book Chapters. ALT 2011 - PDF Preprint Papers | Sciweavers . 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. Noise," International Conference on Algorithmic Learning Theory. Download free ebooks rapidshare, usenet,bittorrent.