A New Support Vector Machine and Its Learning Algorithm

Support vector machine is a learning technique based on the structural risk minimization principle, this paper proposes a new kind of support vector machine (SVM), which modifies the classical SVM formulation to get even simpler dual optimization problem, then gives a quadratic optimization theorem, and according to it derives a multiplicative updates algorithm for solving the dual optimization problem. The updates algorithms converge monotonically to the solution of the optimal problem, and have a simple closed form. Experimental results of simulation indicate the feasibility of the varied regression support vector machine and its training algorithm