基于主动学习的支持向量机算法

针对支持向量机（svM）模型不能有效处理海量数据挖掘的问题，提出一种改进的基于主动学习的支持向量机（AL_SVM）方法。该方法首先将训练集随机划分为多个独立同分布的子集，并选择其中一个子集作为初始训练集来训练SVM得到初始分类器和支持向量集，然后根据已经得到的分类器信息在剩余样本集中选择对于分类器改进作用最大的有价值样本。并与已得到的支持向量集合并构成新训练集，以更新分类器，从而在保留重要支持向量信息的前提下，去除大量不重要的支持向量，一定程度上避免了过学习问题，提高了学习效率。实验表明，AL_SVM方法能够在保持学习器泛化能力的同时提高其学习效率。

Support vector machine algorithm based on active learning

To solve the problems that the support vector machine （SVM） moedel can not process the massive dataset mining effectively, an improved support vector machine based on active learning （AL_SVM） algorithm is presented in this paper. The training set is divided randomly into some independent and identical subsets and only a subset of them is selected as the training set of SVM model to obtain the initial classifier and support vectors set, and then a most valuable sample is selected from the rest of samples by the former classifier to improve the learner and it is combined with the support vector set of the former SVM training to train a new SVM model, soas to improve the classifier. By this method, the important support vectors are retained and the unimportant support vectors are deleted. Therefore, the over-fitting problem can be avoided, and the training efficiency is improved by this method. Simulation results demonstrate that the AL_SVM method can maitain the learner＇s generalization ability and improve the learning efficiency simultaneously.