Polynomial Smooth Support Vector Machine(PSSVM)

Data classification is an important issue of research on data mining. According to the sample dataset, we can build an mathematical model and get the optimal classifier. Then use this classifier to classify the unclassified data points. Support vector machine(SVM) is the main classification model of two classification. The result of support vector machine model is separating surface called support vector. In 2001, Lee and Mangasarian presented the smooth support vector machine(SSVM) which used the integral of Sigmoid function as smoothing function. In this paper, authors research the so called PSSVM which uses the polynomial functions to smoothen the objective function and present two polynomial functions. According to the features of PSSVM, authors use the BFGS and Newton Armijo methods to implement the experiment and show that PSSVM is better than SSVM.