How exactly are Relevant Vector Machines different from Logistic Regression and SVMs' for classification purposes? Concretely, in the case of Logistic Regression, we are looking for a hyper-plane that divides the two classes, and with SVMs', we are looking for optimal large margin Hyper-plane. What exactly are we doing with RVMs'?
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