基于增量学习的支持向量机集成
首发时间:2010-10-15
摘要:为提高支持向量机的泛化能力,提出一种基于增量学习的支持向量机集成方法。该方法利用支持向量集和初始训练样本的等价关系,把支持向量集作为历史训练结果和增量样本一起进行训练,加大了支持向量集的分布,能够提高基支持向量机的分类精度。实验结果显示,该方法可以有效地改善集成效果,具有更好的泛化性能。
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Ensemble Support Vector Machine Based on Incremental Learning
Abstract:In order to improve the generalization performance of support vector machine (SVM), a kind of ensemble SVM using an incremental learning method was proposed. The method train the support vector set as historical training results with the incremental samples because of the equivalence between support vector set and initial training samples, the distribution of support vector set is increased so that the classification accuracy of single SVM can be improved. Simulation results on UCI testing datasets show that the proposed ensemble method can improve the classification precision of SVM and make the ensemble SVM has better generalization property.
Keywords: Support vector machine Ensemble learning Incremental learning
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