Abstract:Relevance vector machine (RVM) is a state-of-theart technique for regression and classification, as a sparse Bayesian extension version of the support vector machine. The selection of a kernel and associated parameter is a critical step of RVM application. The real-world application and recent researches have emphasized the requirement to multiple kernel learning, in order to boost the fitting accuracy by adapting better the characteristics of the data. This paper presents a data-driven evolutionary approach, … Show more
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