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基于活跃集的支持向量机切平面法
  • ISSN号：0372-2112
  • 期刊名称：电子学报
  • 时间：2013.4.15
  • 页码：757-762
  • 分类：TN911.23[电子电信—通信与信息系统;电子电信—信息与通信工程]
  • 作者机构：[1]清华大学自动化系,北京100084
  • 相关基金：国家自然科学基金（No.61225008,No.61020106004,No.61021063,No.61005023）
  • 相关项目：生物特征识别中的新方法及其应用
作者： 肖锋|周杰|
关键词： 切平面法, 支持向量机, 优化切平面法, 活跃集, cutting plane algorithm, support vector machine, optimized cutting plane algorithm, active set
中文摘要：

切平面法作为求解非光滑凸优化问题的典型方法，在支持向量机问题的求解中得到了广泛的应用．但是该算法在求解过程中往往会出现不稳定的情况．针对这一不稳定性，前人提出了优化切平面法，通过在切平面法中加入线搜索环节来确保目标函数单调下降．但是优化切平面法的运算复杂度比较高，不适合训练数据量大、对训练速度要求高的应用．本文提出了一种基于活跃集的优化切平面法，在计算目标函数和进行线搜索时，只单独处理活跃集内的样本，将其它样本当作一个整体来进行处理．相对于传统的优化切平面法，本文方法只需在一部分样本上计算目标函数和进行线搜索，从而可以在不损失求解精度的前提下节省求解时间．

英文摘要：

As a typical method for solving non-smooth convex optimization problems,cutting plane method is widely used in solving support vector machine problems. However, this algorithm suffers from the instability problem. To ease this instability,re- searchers proposed an optimized cutting plane algorithm which incorporated a line search stage. However, the computational com- plexity of such algorithm is too high for applications where the number of training samples is large. In this paper we propose an ac- tive set based optimized cutting plane algorithm to reduce the computation complexity of the original algorithm. When computing the objective function and performing line search, only those samples which fall in the active set are considered. Compared to opti- mized cutting plane algorithm, the proposed algorithm needs to calculate the objective function and perform line search only for a small fraction of the samples, leading to a sijznificant drop in comoutational comolexity without losing accuracy.

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期刊信息
  • 《电子学报》
  • 中国科技核心期刊
  • 主管单位:中国科学技术协会
  • 主办单位:中国电子学会
  • 主编：郝跃
  • 地址：北京165信箱
  • 邮编：100036
  • 邮箱：new@ejournal.org.cn
  • 电话：010-68279116 68285082
  • 国际标准刊号：ISSN：0372-2112
  • 国内统一刊号：ISSN：11-2087/TN
  • 邮发代号:2-891
  • 获奖情况:
  • 2000年获国家期刊奖,2000年获国家自然科学基金志项基金支持,中国期刊方阵“双高”期刊
  • 国内外数据库收录:
  • 美国化学文摘（网络版）,荷兰文摘与引文数据库,美国工程索引,美国剑桥科学文摘,日本日本科学技术振兴机构数据库,中国中国科技核心期刊,中国北大核心期刊（2004版）,中国北大核心期刊（2008版）,中国北大核心期刊（2011版）,中国北大核心期刊（2014版）,英国英国皇家化学学会文摘,中国北大核心期刊（2000版）
  • 被引量:57611