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

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

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. 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. Share this I'm a bit of a freak – enterprise software team lead during the day and neural network researcher during the evening. 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. 10th International Conference on Inductive Logic Programming,. Ci-dessous donc la liste de mes bouquins favoris sur le sujet:A theory of learning an… Hébergé par OverBlog. Cite as: arXiv:1303.0818 [cs.NE]. This important work describes recent theoretical advances in the study of artificial neural networks. Neural Network Learning: Theoretical foundations, M. In this book, the authors illustrate an hybrid computational Table of contents. Neural Network Learning: Theoretical Foundations: Martin Anthony. 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.