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In this paper, we introduce the relevant vector machine (RVM) from Michael Tipping. The formulation of the RVM in regression and classification is reviewed. Then we analyze why the RVM can reach a sparse solution. In the experiment, we use the real application data to compare the performance of SVM and RVM. The advantages and disadvantage of the SVM and RVM is analyzed based on the experimental results. Some suggestion for the RVM is presented in the discussion section.
Control, Automation, Robotics and Vision Conference, 2004. ICARCV 2004 8th (Volume:2 )
Date of Conference: 6-9 Dec. 2004