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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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Proceedings ArticleDOI
Evaluating algorithm performance metrics tailored for prognostics
TL;DR: This paper introduces several new evaluation metrics tailored for prognostics and shows that they can effectively evaluate various algorithms as compared to other conventional metrics.
Posted Content
Efficient Hyperparameter Optimization of Deep Learning Algorithms Using Deterministic RBF Surrogates
TL;DR: In this article, a new deterministic and efficient hyperparameter optimization method that employs radial basis functions as error surrogates is proposed, called HORD, which is more than 6 times faster than GP-EI in obtaining the best configuration of 19 hyperparameters.
Journal ArticleDOI
A Gaussian process-based RRT planner for the exploration of an unknown and cluttered environment with a UAV
TL;DR: Simulation results show that GP map combined with RRT planner can achieve the 3D navigation and exploration task successfully in unknown and complex environments.
Journal ArticleDOI
Online quality prediction of nonlinear and non-Gaussian chemical processes with shifting dynamics using finite mixture model based Gaussian process regression approach
TL;DR: The proposed FMM–GPR approach is applied to the Tennessee Eastman Chemical process with multiple operating modes and its performance is compared to that of the multi-model LSSVM method using two test cases.
Proceedings Article
PAC-Bayesian theory meets Bayesian inference
TL;DR: For the negative log-likelihood loss function, it is shown that the minimization of PAC-Bayesian generalization risk bounds maximizes the Bayesian marginal likelihood.
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