We argue that this manual system is far from ideal, and can be improved using machine learning techniques. We propose a system based on Gaussian process regression for improving the efficacy of ...

Abstract: Gaussian processes regression models are an appealing machine learning method as they learn expressive nonlinear models from exemplar data with minimal parameter tuning and estimate both the ...

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Dirichlet process mixture models (DP-MM) are a generalization of the Dirichlet process to multiple components. The Dirichlet process is a probabilistic model that assumes that a finite number of ...