Algorithm And Application Research Of Support Vector Machine Regression

Data based machine learning is an important topic of modern intelligent techniques. Statistical Learning Theory or SLT is a small-sample statistics, which concerns mainly the statistic principles when sample are limited. Especially the properties of learning procedure in such cases. SLT provides us a new framework for the general learning problem and a novel powerful learning method called Support Vector Machine or SVM, which can solve small- sample learning problems better. It has many advantages compared to Article Neural Networks or other learning methods, for example the automatic structure selecting, overcoming the local minimum and over-fitting etc.Most of the research works focuse on the Support Vector Machine classify theory and application, and the recently research works on Support Vector Machine Regression or SVMR also show its excellent performance. As a novel theory and method, the training algorithm, practical application and many other topics of SVMR are need to be discussed.This dissertation concentrated on the research work listed below and achieved some creative results.(1) Based on good understanding of the Support Vector Machine Regression (SVR) theory and algorithm, an Online Support Vector Machine Regression (OSVR) algorithm is proposed. Bach implementations of Support Vector Regression are inefficient when used in an online setting, because they must be retrained from scratch every time the training set is modified. This paper presents an online support vector regression for regression problems that have input data supplied in sequence rather than in batch. The OSVR has been applied to two benchmark problems shows that the OSVR algorithm has a much faster convergence and results in a smaller number of support vectors and a better generalization performance in comparison with the existing algorithms.(2) After the analysis and comprehension of industrial process soft sensing, we introduce the support vector machine method into the soft sensing of Kappa number of kraft pulping process. Aiming at the problem of predicting Kappa number of kraft pulping process under circumstances of complicated process kinetics and poor basic information, the support vector machine method was introduced. The basic theory and algorithm of the method were presented and...