支撑向量机多类分类方法的研究

Research on Multi-class Classification of Support Vector Machine

支撑向量机是一种基于统计学理论的新的学习算法,它采用了结构风险最小化原则,能有效地解决过学习问题,具有很强的泛化能力。传统支撑向量机针对两类分类问题,为了深入地分析实际应用中的大规模和多类别的问题,通过对一次性求解法、一对多、一对一、有向无环图方法的原理和实现方法进行分析,从速度和精度两方面对这些方法的优缺点进行了归纳和总结,并通过实验进行了验证和比较。实验结果表明,各种方法可以获得不同的分类器推广能力及训练速度和测试速度,也为今后如何更好地解决支撑向量机多类分类问题指明了方向。

Support vector machine（SVM） is a new learning method based on statistical learning theory, which can effectively solve the over study problem by using structural risk minimization （SRM） and has better generalization performance. Traditional SVM is developed for binary classification problems, in order to analyze huge and multi-category data for practical problems, a comparison result about the classification speed and accuracy is given through analyzing the theory and realization method of all-together, one-against-rest, one-against-one and directed acyclic graph sup- port vector machine（DAGSVM）. Experimental results show that various methods can get different classifier generalization ability, training speed and test speed. The direction of how to solve multi-class classification effectively is proposed.