An SMO Algorithm for the Potential Support Vector Machine

We describe a fast Sequential Minimal Optimization (SMO) procedure for solving the dual optimization problem of the recently proposed Potential Support Vector Machine (P-SVM). The new SMO consists of a sequence of iteration steps, in which the Lagrangian is optimized either with respect to one (single SMO) or to two (dual SMO) of the Lagrange multipliers while keeping the other variables fixed. An efficient selection procedure for Lagrange multipliers is given and two heuristics for improving the SMO procedure are described: block optimization and annealing of the regularization parameter . A comparison between the different variants show, that the dual SMO including block optimization and annealing performs most efficient in terms of computation time. In contrast to standard Support Vector Machines (SVMs), the P-SVM is applicable to arbitrary dyadic datasets, but benchmarks are provided against libSVM's -SVR and C-SVC implementations for problems which are also solvable by stand...

• Type: Journal
• Year: 2008
• Where: NECO
• Authors: Tilman Knebel, Sepp Hochreiter, Klaus Obermayer
• Keywords: Dual Smo, Lagrange Multipliers, NECO 2008, Support Vector Machines