1# Library to Find Best eBook for Read
Author : Julia Vinogradska
Release : 2018
Genre :
Kind : eBook
Book Rating : /5 ( reviews)
Book Synopsis Gaussian Processes in Reinforcement Learning: Stability Analysis and Efficient Value Propagation by : Julia Vinogradska
Download or read book Gaussian Processes in Reinforcement Learning: Stability Analysis and Efficient Value Propagation written by Julia Vinogradska. This book was released on 2018. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Marc Peter Deisenroth
Release : 2010
Genre : Electronic computers. Computer science
Kind : eBook
Book Rating : 695/5 ( reviews)
Book Synopsis Efficient Reinforcement Learning Using Gaussian Processes by : Marc Peter Deisenroth
Download or read book Efficient Reinforcement Learning Using Gaussian Processes written by Marc Peter Deisenroth. This book was released on 2010. Available in PDF, EPUB and Kindle. Book excerpt: This book examines Gaussian processes in both model-based reinforcement learning (RL) and inference in nonlinear dynamic systems.First, we introduce PILCO, a fully Bayesian approach for efficient RL in continuous-valued state and action spaces when no expert knowledge is available. PILCO takes model uncertainties consistently into account during long-term planning to reduce model bias. Second, we propose principled algorithms for robust filtering and smoothing in GP dynamic systems.
Author : Carl Edward Rasmussen
Release : 2005-11-23
Genre : Computers
Kind : eBook
Book Rating : 53X/5 ( reviews)
Book Synopsis Gaussian Processes for Machine Learning by : Carl Edward Rasmussen
Download or read book Gaussian Processes for Machine Learning written by Carl Edward Rasmussen. This book was released on 2005-11-23. Available in PDF, EPUB and Kindle. Book excerpt: A comprehensive and self-contained introduction to Gaussian processes, which provide a principled, practical, probabilistic approach to learning in kernel machines. 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 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 classical perspective. Many connections to other well-known techniques from machine learning and statistics are discussed, including support-vector machines, neural networks, splines, regularization networks, relevance vector machines and others. Theoretical issues including learning curves and the PAC-Bayesian framework are treated, and several approximation methods for learning with large datasets are discussed. The book contains illustrative examples and exercises, and code and datasets are available on the Web. Appendixes provide mathematical background and a discussion of Gaussian Markov processes.
Author : Ralf Herbrich
Release : 2022-11-01
Genre : Computers
Kind : eBook
Book Rating : 590/5 ( reviews)
Book Synopsis Learning Kernel Classifiers by : Ralf Herbrich
Download or read book Learning Kernel Classifiers written by Ralf Herbrich. This book was released on 2022-11-01. Available in PDF, EPUB and Kindle. Book excerpt: An overview of the theory and application of kernel classification methods. Linear classifiers in kernel spaces have emerged as a major topic within the field of machine learning. The kernel technique takes the linear classifier—a limited, but well-established and comprehensively studied model—and extends its applicability to a wide range of nonlinear pattern-recognition tasks such as natural language processing, machine vision, and biological sequence analysis. This book provides the first comprehensive overview of both the theory and algorithms of kernel classifiers, including the most recent developments. It begins by describing the major algorithmic advances: kernel perceptron learning, kernel Fisher discriminants, support vector machines, relevance vector machines, Gaussian processes, and Bayes point machines. Then follows a detailed introduction to learning theory, including VC and PAC-Bayesian theory, data-dependent structural risk minimization, and compression bounds. Throughout, the book emphasizes the interaction between theory and algorithms: how learning algorithms work and why. The book includes many examples, complete pseudo code of the algorithms presented, and an extensive source code library.
Author : Brendan J. Frey
Release : 1998
Genre : Computers
Kind : eBook
Book Rating : 022/5 ( reviews)
Book Synopsis Graphical Models for Machine Learning and Digital Communication by : Brendan J. Frey
Download or read book Graphical Models for Machine Learning and Digital Communication written by Brendan J. Frey. This book was released on 1998. Available in PDF, EPUB and Kindle. Book excerpt: Content Description. #Includes bibliographical references and index.
