An Improved Training Algorithm of Support Vector Machines Based on Three Data Points Iteration

Support vector machines (SVM) is an excellent method of machine learning. It can be reduced to solving large-scale quadratic programming problem. The Sequential Minimal Optimisation (SMO) algorithm is the best training algorithm for SVM, it optimizes a minimal subset of just two points at each iteration. This paper proposes an improved training algorithm on the premise of the optimization problem admits an analytical solution. The improved algorithm optimizes three points at each iteration. The modified algorithm performs significantly faster than the original SMO on the datasets tried.