针对传统的分类算法以及精度作为评价指标不能够满足现实分类问题的需要,将代价敏感方法引入支持向量机中,提出一种新的学习算法CSSVM,并得到了类似于Pegasos的投影次梯度求解方法,用于大规模数据的处理.Pegasos的步骤包括初始化,迭代,确定梯度下降的步长、确定梯度下降方向、更新,投影和结束.实验结果表明,该算法能有效提高识别率和识别精度,具有一定的竞争力.%Aiming at the traditional method and its precision which used as evaluation index can not meet the requirements of practical classification. Introduce cost sensitive method into SVM, put forward a new learning algorithm CSSVM (cost-sensitive SVM), and acquire projection sub-gradient solving method which is similar as Pegasos to deal with large scale data. The Pegasos process includes initialization, iteration, ascertaining step lengths and direction of sub-gradient descent, update, projection and the end. The test results show that this algorithm can effectively improve identifying rate and identifying precision and it is competitive.
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