Research On Support Vector Machine Algorithm And Its Application

Support vector machine (SVM) based on the Statistical Learning Theory is a new approach and research field in machine learning because of its advantage such as firm mathematic theory foundation, strict theory analysis, complete theory, global optimization as well as good adaptability and generalization. SVM improves the algorithm generalization effectively and minimizes the empirical risk simultaneously by using Structural Risk Minimization and synthesizing the techniques including the statistical learning, machine learning and neural networks, etc. It also has good latent application values and development prospects compared with the conventional machine learning methods.In this paper, several typical support vector machine algorithms are generalized. Five novel algorithms are proposed. They are PCA support vector machine algorithm which is based on the idea of combination multi-class classification, weighted PCA support vector machine algorithm, wavelet support vector machine borrowed idea from the kernel function, RS-SVM dynamic prediction and fuzzy binary tree support vector machine. The performance and applications of the algorithms are studied in depth. The research is carried out in the following aspects:1. The solution methods of support vector machine, including quadratic programming method, chunking method, decomposing method, sequential minimization optimization method, iterative solution method named Lagrange support vector machine based on Lagrange function and Newton method based on the smoothing technique, are studied systematically. The methods employs solving convex quadratic programming directly or solving convex quadratic programming after converting the large-scale problem into many sub-problem or utilizing sophisticated optimization techniques after converting the constrained optimization problem into unconstrained ones. The theory foundation for presenting new support vector machine algorithms is laid by means of analyzing those methods.2. Three support vector machines based on L_p-norm classification margin are studied. The algorithms theory and realization of the L₁-norm SVM under linear and nonlinear conditions are emphatically studied. The optimization problem of L₁-norm SVM which adopts the L_p-norm to measure the classification margin is analyzed. In high dimensions feature space, L₁-norm SVM shows better feature compression...