Training and analysis of Support Vector Machine using Sequential Minimal Optimization

Maximizing the classification performance of the training data is a typical procedure in training a classifier. It is well known that training a Support Vector Machine (SVM) requires the solution of an enormous quadratic programming (QP) optimization problem. Serious challenges appeared in the training dilemma due to immense training and this could be solved using Sequential Minimal Optimization (SMO). This paper investigates the performance of SMO solver in term of CPU time, number of support vector and decision boundaries when applied in a 2-dimensional datasets. Next, the chunking algorithm is employed for comparison purpose. Initial results demonstrated that the SMO algorithm could enhance the performance of the training dataset. Both algorithms illustrated similar patterns from the decision boundaries attained. Classification rate achieved by both solvers are superb.