Support Vector Machines: Sequential Multidimensional Subsolver (SMS)

In this paper I will present a new algorithm for solving Support Vector Machines (SVM) optimization problem. The new algorithm has a simpler form, than existing algorithms and has a comparable computational cost. Classical Sequential Minimal Optimization (SMO) algorithm decomposes SVM problem into two dimensional subproblems. It was shown in [3], that SVM optimization with decomposition into more than two dimensional subproblems can be faster. However existing algorithms for solving multidimensional subproblems are complicated quadratic programming solvers. Proposed Sequential Multidimensional Subsolver (SMS) employs SMO for solving multidimensional subproblems. Tests show, that SVM solver with SMS is generally faster, than SMO algorithm.