文件名称：Sphere-Structured Support Vector Machines for Multi-class Pattern Recognition
文件大小：68KB
文件格式：PDF
更新时间：2012-10-01 09:32:22
Sphere-Structured
超球结构支持向量机用于多分类问题。Support vector machines (SVM) are learning algorithms derived from statistical learning theory. The SVM approach was originally developed for binary classification problems. For solving multi-class classification problem, there are some methods such as one-against-rest, one-against-one, all- together and so on. But the computing time of all these methods are too long to solve large scale problem. In this paper SVMs architectures for multi-class problems are discussed, in particular we provide a new algorithm called sphere- structured SVMs to solve the multi-class problem. We show the algorithm in detail and analyze its characteristics. Not only the number of convex quadratic programming problems in sphere-structured SVMs is small, but also the number of variables in each programming is least. The computing time of classification is reduced. Otherwise, the characteristics of sphere-structured SVMs make expand data easily.