Title :
Comparison of the support vector machine and relevant vector machine in regression and classification problems
Author :
Yu, Wei Miao ; Du, Tiehua ; Lim, Kah Bin
Author_Institution :
Nat. Univ. of Singapore, Singapore
Abstract :
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.
Keywords :
pattern classification; regression analysis; support vector machines; classification problem; regression analysis; relevant vector machine; sparse solution; support vector machine; Gaussian processes; Guidelines; Iterative methods; Kernel; Machine learning; Predictive models; Robustness; Support vector machine classification; Support vector machines;
Conference_Titel :
Control, Automation, Robotics and Vision Conference, 2004. ICARCV 2004 8th
Print_ISBN :
0-7803-8653-1
DOI :
10.1109/ICARCV.2004.1469035
