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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 Article
Approximations for Binary Gaussian Process Classification
TL;DR: A comprehensive overview of many recent algorithms for approximate inference in Gaussian process models for probabilistic binary classification and the relationships between several approaches are elucidated theoretically, and the properties of the different algorithms are corroborated by experimental results.
Journal ArticleDOI
Robust PCA via Outlier Pursuit
TL;DR: In this paper, an efficient convex optimization-based algorithm that is called outlier pursuit is presented, which under some mild assumptions on the uncorrupted points (satisfied, e.g., by the standard generative assumption in PCA problems) recovers the exact optimal low-dimensional subspace and identifies the corrupted points.
Journal ArticleDOI
Learning to select and generalize striking movements in robot table tennis
TL;DR: In this paper, a robot learns a set of elementary table tennis hitting movements from a human table tennis teacher by kinesthetic teach-in, which is compiled into a mixture of motor primitives represented by dynamical systems.
Journal ArticleDOI
Adaptive Regularization of Weight Vectors
TL;DR: Empirical evaluations show that AROW achieves state-of-the-art performance on a wide range of binary and multiclass tasks, as well as robustness in the face of non-separable data.
Journal ArticleDOI
Counting People With Low-Level Features and Bayesian Regression
Antoni B. Chan,Nuno Vasconcelos +1 more
TL;DR: An approach to the problem of estimating the size of inhomogeneous crowds, which are composed of pedestrians that travel in different directions, without using explicit object segmentation or tracking is proposed, using the mixture of dynamic-texture motion model and Bayesian regression.
References
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The Nature of Statistical Learning Theory
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