by Abdelhamid Mellouk, Abdennacer Chebira
Publisher: InTech 2009
Number of pages: 450
Neural machine learning approaches, Hamiltonian neural networks, similarity discriminant analysis, machine learning methods for spoken dialogue simulation and optimization, linear subspace learning for facial expression analysis, 3d shape classification and retrieval, genetic network programming with reinforcement learning, heuristic dynamic programming, and more.
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by Hal Daumé III - ciml.info
Tis is a set of introductory materials that covers most major aspects of modern machine learning (supervised and unsupervised learning, large margin methods, probabilistic modeling, etc.). It's focus is on broad applications with a rigorous backbone.
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Gaussian processes provide a principled, practical, probabilistic approach to learning in kernel machines. The treatment is comprehensive and self-contained, targeted at researchers and students in machine learning and applied statistics.
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This book provides an introduction to statistical learning methods. It contains a number of R labs with detailed explanations on how to implement the various methods in real life settings and it is a valuable resource for a practicing data scientist.
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