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Gaussian Processes for Machine Learning
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TLDR
The treatment is comprehensive and self-contained, targeted at researchers and students in machine learning and applied statistics, and deals with the supervised learning problem for both regression and classification.Abstract:
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.read more
Citations
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Journal ArticleDOI
A Survey of Multi-View Representation Learning
TL;DR: Multi-view representation learning has become a rapidly growing direction in machine learning and data mining areas as mentioned in this paper, and a comprehensive survey of multi-view representations can be found in this paper.
Journal ArticleDOI
Ensemble SVM Method for Automatic Sleep Stage Classification
TL;DR: Classification performance results indicate that, it is possible to have an efficient sleep monitoring system with a single-channel EEG, and can be used effectively in medical and home-care applications.
Proceedings Article
Accelerating Bayesian Inference over Nonlinear Differential Equations with Gaussian Processes
TL;DR: This work presents an accelerated sampling procedure which enables Bayesian inference of parameters in nonlinear ordinary and delay differential equations via the novel use of Gaussian processes (GP).
Proceedings ArticleDOI
Manifold Gaussian Processes for regression
TL;DR: Manifold Gaussian Processes is a novel supervised method that jointly learns a transformation of the data into a feature space and a GP regression from the feature space to observed space, which allows to learn data representations, which are useful for the overall regression task.
Journal ArticleDOI
Extended Kernel Recursive Least Squares Algorithm
TL;DR: A kernelized version of the extended recursive least squares (EX-KRLS) algorithm which implements for the first time a general linear state model in reproducing kernel Hilbert spaces (RKHS) which only requires inner product operations between input vectors, thus enabling the application of the kernel property.
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