基于改进支持向量机的信息融合算法及应用研究

Yan-hui Wang, Chenchen Zhang, Jun Luo
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引用次数: 2

摘要

提出了一种基于改进支持向量机的信息融合算法,即决策树-支持向量机算法(decision tree Method-Support vector machines, DTM-SVM)。该算法克服了传统支持向量机分类方法以“一对多”模式只适用于两类问题的局限性,解决了多类问题,满足了更广泛的应用需求。最后,在建立高速公路交通状态识别评价体系的基础上,应用DTM-SVM模型求解高速公路交通状态识别问题。结果表明:该算法可以在较短的时间内进行识别,达到较高的识别精度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Study on information fusion algorithm and application based on improved SVM
Authors presented the information fusion algorithm based on improved SVM, namely, decision tree - support vector machine algorithm (Decision Tree Method-Support Vector Mechines, DTM-SVM). The algorithm overcame the limitations of the conventional SVM classification which applied only to two-classification problem by a “one to many” pattern, solved multi-classification problem and met a wider range of application requirements. Finally, based on the establishment of a freeway traffic state identification evaluation system, the DTM-SVM model was applied to solve the freeway traffic state recognition. Results show that: the algorithm can identify in a shorter time to reach higher recognition accuracy.
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