支持向量机的研究进展  

The research progress of support vector machine

作　　者：刘昊[1] 扶炜[1] 

机构地区：[1]信阳农林学院计算机科学系,河南信阳464000

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

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.

关 键 词：统计学习 支持向量机 进展 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]