Research Of Learning Methods On Single-class Support Vector Machine

Support Vector Machine (SVM) is a novel machine learning method based on the framework of Statistical Learning Theory. SVM minimizes the empirical risk and confidence interval simultaneously by using Structural Risk Minimization, which holds the advantages of high fitting accuracy, few parameters, good generalization and convergence to global optimum. SVM provides a valid tool in dealing with small-sample, high-dimensional and nonlinear problems, so it has become one of the hotpots of machine learning reseach and been widely applied in many areas.SVM is proposed for binary classification problems, and two categories of samples are necessary for classifier training; however, in some fields of practical applications, obtaining two categories of samples is almost impossible or extremely cost, for example, enemy-friend recognition, attack samples, satellite fault diagnosis, etc. Only one class of samples can be acquired, so the data description has to be learned from this type of samples for classification. The classification problem is named as single-class classification.Support Vector Data Description (SVDD) and One-class Support Vector Machine (OCSVM) are two algorithms of single-class classification extended by SVM. The two algorithms are completely equivalent if using the same Gaussian kernel function, and be together called as Single-class Support Vector Machine (1-SVM)."A workman must first sharpen his tools if he is to do his work well." To make1-SVM more effective in practical engineering problems, first of all, its training or learning problems should be solved, which is actually the process of solving a quadratic programming (OP) problem. In this paper, the goal is to enhance the1-SVM’s learning ability. Three aspects of particle swarm optimization learning, fast learning and incremental learning on1-SVM are respectively studied, and corresponding solutions are put forward. The main work is as follows:Linear Particle Swarm Optimization algorithm (LPSO), which is an extension of Particle Swarm Optimization (PSO) algorithm, is proposed to apply to1-SVM learning. Because the particles flying to the group optimal position are easy fall into stagnation, PSO and LPSO have the problem of premature convergence. In order to solve this, the strategy of changing the flight mode of group optimal particle is adopt, and group optimal particle flies in different ways with other particles. The algorithm improves the LPSO’s convergence, and is applied to the process of LPSO learing1-SVM. LPSO provides a new idea to solve1-SVM learning.For1-SVM’s learning problem of large-scale sample set, inspired by the successful application of Random Sampling Lemma (RSL) in convex quadratic programming problem, a Fast Learning (FL) method is proposed based on RSL. Two sample subsets are randomly selected from large-scale sample set. Based on RSL a and deduced Combining Lemma, the support vectors (extreme) and samples violating the KKT (violator) of each other are fused, then the common decision boundary of two subsets are produced. And so on, until the extraction and integration of all samples are completed. This method breaks the large-scale dataset into subsets at random and learns1-SVM to each subset. It reduces the memory space and computing time of1-SVM learning, so is an effective method of fast learning.In order to achieve incremental learning process of1-SVM, the geometry characteristics of OCSVM are analyzed, and an Incremental Learning (IL) method based on Delta Function is proposed. Because OCSVM has only one hyper-plane in its geometric structure, a new decision function (that is a new classification hyper-plane) will be formed when a Delta Function is added to the decision function of its classification hyper-plane. Solving the Delta Function according to the new sample is the process of Incremental Learning of OCSVM. Inspired by the quadratic programming problem of OCSVM, analyzing the optimization problem of the Delta Function is a quadratic programming problem too, and the modified sequential minimal optimization (SMO) algorithm proposed to solve this problem.The components of license plate recognition system are introduced in brief. The characters of high-definition license plate recognition are analyzed, and the corresponding solutions to core technologies of plate localization, segmentation and character recognition are proposed. One-against-all multi-classification based on1-SVM is applied to character recognition. Recognition ability of1-SVM is improved by the proposed incremental learning method. Finally, the system achieved through C++programming shows that it is successful.