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多类扩码支撑矢量机

Multi-class SVM Based On Extended Code
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摘要 本文通过扩展类别标示符二进制编码,提升决策空间的维数以增强决策函数的分类能力,提出了一种新的多类扩码支撑矢量机模型——半对半算法(Half-Versus-Half)。该模型通过序贯解耦求解,有效地提高了运算速度,同时克服了测试过程中的判决"死区"问题。另外,基于统计学习理论,本文还分析和探讨了多类目标扩码识别算法的推广性能。实测数据实验结果表明,半对半算法在计算速度和识别性能上有了明显的提高。 This paper puts forward a multi-class SVM based on extending the dimension of decision space, which is called Half- Versus-Half （H-V-H） algorithm. The H-V-H model can be sequentially solved and has no decision blind region. In addition, the paper has analyzed the generalized performance of multi-class objection recognition algorithm based on extended code. As the experimental re- sults shown, validated with the actually measuring data, the performance of the H-V-H method, such as classification capability and generalized capability are improved obviously in contrast to the classical method.
作者 任双桥 王宏强 黎湘 庄钊文
机构地区 国防科技大学电子科学与工程学院空间电子信息技术研究所
出处 《信号处理》 CSCD 北大核心 2009年第6期916-924,共9页 Journal of Signal Processing
基金 国家自然科学基金(No.60402032) 国防预研项目(No.41303040203)
关键词 多类目标 支撑矢量机 扩码 半对半算法 multi-class objection support vector machines extended code half-versus-half
分类号 TP18 [自动化与计算机技术—控制理论与控制工程]
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参考文献13

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二级参考文献19

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共引文献278

信号处理
2009年 第6期

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