支持向量机中优化算法

1 引言对基于数掘的机器学习研究是目前计算智能技术的基础性工作之一,旨在从观测数据出发基于统计学理论采用相关技术对研究对象建立可信的模型,利用这一模型对未来实验作出指导性的预测。相关技术包括聚类分析、模式识别、神经网络等等。现有的学习方法多是基于样本数趋于无穷大时的渐进理论。可是在实际科研工作中,有时候很难获得较为充足

A Survey of Optimization Algorithms in Support Vector Machine

Optimization algorithm solving Lagrangian multipliers is the key of training SVM, determining the performance of SVM, affecting practical applications of SVM in various fields widely. Some kinds of optimization algorithms in SVM of overseas are introduced. We classify the optimization algorithms into two kinds : 1. the algorithms based on Osuna's decomposition strategy; 2. The iterative algorithms based on the changes of SVM formulation proposed by O. L. Mangasanan. We also analyze the characteristics of various optimization algorithms in SVM,and predicting the trend of research on optimization algorithm in SVM.