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一种求解分类问题的新算法
  • 期刊名称：运筹学学报,2(2008),p41-48.
  • 时间：0
  • 分类：TP311.13[自动化与计算机技术—计算机软件与理论;自动化与计算机技术—计算机科学与技术] TP18[自动化与计算机技术—控制科学与工程;自动化与计算机技术—控制理论与控制工程]
  • 作者机构：[1]中国农业大学理学院,北京100083
  • 相关基金：Supported by the Key Project of the National Natural Science Foundation of China（No.10631070） and the National Natural Science Foundation of China（60573158）, 本文获北京运筹学会2005年青年优秀论文二等奖.
  • 相关项目：图像处理中最优化方法的研究
关键词： 运筹学, 线性规划, 支持向量机, 模, 无参数化, Operations research, linear programming, support vector machines, norm, parameterless
中文摘要：

本文提出了基于支持向量回归机（SVR）的一种新分类算法．它和标准的支持向量机（SVM）不同：标准的支持向量机（SVM）采用固定的模度量间隔且最优化问题与参数有关．本文中我们可以用任意模度量间隔，得到的最优化问题是无参数的线性规划问题，避免了参数选择．数值试验表明了该算法的有效性．

英文摘要：

A new algorithm to solve classification problems is obtained based on the support vector regression （SVR）. It is different form the standard Support vector machine（SVM）： The margin is measured by fixed norm and the optimization problems depend on the parameters in the standard SVM. In this paper, we can measure the margin by arbitrary norm, and the deduced optimization problem is a linear programming without parameters. Preliminary experiments also show the validity of our new algorithm.

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