Abstract:Abstract. Relevance vector machine (RVM) is a machine learning technique that uses Bayesian inference to obtain parsimonious solutions for regression and probabilistic. Compared to support vector machines (SVM), the Bayesian formulation of the RVM avoids the set of free parameters of the SVM. However, the classification accuracy of RVM is not high when apply to hyperspectral data. A novel classification method based on RVM is presented in this paper. The method combine principal component analysis (PCA) and li… Show more
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