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Neural Network Learning: Theoretical Foundations Martin Anthony, Peter L. Bartlett
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Biggs — Computational Learning Theory; L. Some titles of books I've been reading in the past two weeks: M. 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. Although this blog includes links to other Internet sites, it takes no responsibility for the content or information contained on those other sites, nor does it exert any editorial or other control over those other sites. Ci-dessous donc la liste de mes bouquins favoris sur le sujet:A theory of learning an… Hébergé par OverBlog. Bartlett — Neural Network Learning: Theoretical Foundations; M. Neural Network Learning: Theoretical foundations, M. Artificial neural networks, a biologically inspired computing methodology, have the ability to learn by imitating the learning method used in the human brain. 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.