Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning series)
Gaussian processes (GPs) provide a principled, practical, probabilistic approach to learning in kernel machines. Get and download textbook Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning series) for free
Categories: Machine learning. Contributors: Carl Edward Rasmussen - Author. Format: Hardcover
GPs have received increased attention in the machine-learning community over the past decade, and this book provides a long-needed systematic and unified treatment of theoretical and practical aspects of GPs in machine learning. The treatment is comprehensive and self-contained, targeted at researchers and students in machine learning and applied statistics.The book deals with the supervised-learning problem for both regression and classification, and includes detailed algorithms. A wide variety of covariance (kernel) functions are presented and their properties discussed. Model selection is discussed both from a Bayesian and a cl Gaussian Processes for Machine Learning new edition
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Gaussian Processes for Machine Learning: Carl Edward Rasmussen, Christopher KI Williams
Categories: Machine learning. Contributors: Carl Edward Rasmussen - Author. Format: Hardcover
author carl edward rasmussen author christopher ki williams format hardback language english publication year 10 01 2006 series adaptive computation and machine learning series subject computing it subject 2 computing professional programming gaussian processes for machine learning rasmussen author biography carl edward rasmussen is a lecturer at the department of engineering university of cambridge and adjunct research scientist at the max planck institute for biological cybernetics tubingen c
Store Search search Title, ISBN and Author Gaussian Processes for Machine Learning by Carl Edward Rasmussen, Christopher KI Williams Estimated delivery 3-12 business days Format Hardcover Condition Brand New Gaussian processes (GPs) provide a principled, practical, probabilistic approach to learning in kernel machines. GPs have received increased attention in the machine-learning community over the past decade, and this book provides a long-needed systematic and unified treatment of theoretic
Gaussian Processes for Machine Learning Textbook
GPs have received increased attention in the machine-learning community over the past decade, and this book provides a long-needed systematic and unified treatment of theoretical and practical aspects of GPs in machine learning. The treatment is comprehensive and self-contained, targeted at researchers and students in machine learning and applied statistics.The book deals with the supervised-learning problem for both regression and classification, and includes detailed algorithms. A wide variety of covariance (kernel) functions are presented and their properties discussed
Ps have received increased attention in the machine-learning community over the past decade, and this book provides a long-needed systematic and unified treatment of theoretical and practical aspects of GPs in machine learning. The treatment is comprehensive and self-contained, targeted at researchers and students in machine learning and applied statistics.The book deals with the supervised-learning problem for both regression and classification, and includes detailed algorithms. A wide variety of covariance (kernel) functions are presented and their properties discussed. Model selection is discussed both from a Bayesian and a cl
