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支持向量机的研究进展 被引量：1

The research progress of support vector machine
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摘要 本文分析总结了支持向量机从提出,兴起到现在的研究成果,并重点关注算法方面的进展。对各个方向的研究都做了相应分析。并适当编程实现了性能优越的序贯最小优化(SMO)算法。最后给出了针对各种应用问题,较为理想的算法选择。 This paper summarizes the evolvement of the support vector machine especially the aspect of algorithm from the starting point to gradual growth by now. All kinds of research are presented with corresponding analysis. Then as its remarkable performance, sequential minimal optimization （SMO） algorithm is simply translated to programming. Finally, some ideal choices of algorithm for specific applications are provided.
作者 刘昊 扶炜
机构地区 信阳农林学院计算机科学系
出处 《信阳农业高等专科学校学报》 2013年第4期110-114,共5页 Journal of Xinyang Agricultural College
关键词 统计学习 支持向量机 进展 statistical learning support vector machine progress
分类号 TP301.6 [自动化与计算机技术—计算机系统结构]
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参考文献9

  • 1Boser B, Guyon I, Vapnik V. A training algorithm for optimal mar- gin classifiers [ A]. Proceedings of the 5th Annual ACM Workshop on Computational Learning Theory [ C ]. New York : ACM Press, 1992 : 144 - 152.
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二级参考文献12

  • 1Ingo Steinwart, On the influence of the kernel on the generalization ability of support vector machines. Department of mathematics and computer science, Friedrich Schiller University(Jena): Technical Report TR-01-01, 2001 (Available as http://www. minet. uni-jena. de /Math-Net /reports/rep-com.html).
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信阳农业高等专科学校学报
2013年 第4期

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