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      TheGaiaastrophysical parameters inference system (Apsis) : Pre-launch description

      , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ,
      Astronomy & Astrophysics
      EDP Sciences

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          GAIA: Composition, formation and evolution of the Galaxy

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            Gaussian Processes for Machine Learning

            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.
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              Kernel Smoothing

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                Author and article information

                Journal
                Astronomy & Astrophysics
                A&A
                EDP Sciences
                0004-6361
                1432-0746
                November 2013
                November 19 2013
                November 2013
                : 559
                : A74
                Article
                10.1051/0004-6361/201322344
                5b21375b-4385-465b-b6b9-ca98ba35fb84
                © 2013
                History

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