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gaussian processes for machine learning bibtex

Concretely, if the output of a GP regressor is mapped onto the interval [0, 1] through a squash function, then this output value represents the probability of a sample belonging to one of the two classes. Machine Learning Summer School 2012: Gaussian Processes for Machine Learning (Part 1) - John Cunningham (University of Cambridge) http://mlss2012.tsc.uc3m.es/ In the final sections of this chapter, these methods are applied to learning in Gaussian process models for regression and classification. The problem is approached in terms of different methodologies, Bayesian principles, cross-validation, and the leave-one-out estimator. The book is concerned with supervised learning, that is, the problem of learning input-output mappings from empirical data. The figure illustrates the cases of noisy observations (variance at training points) and of noise-free observationshttps://i.imgur.com/BWvsB7T.png (no variance at training points). 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