[1]陈晓华,刘大莲,田英杰,等.可拓支持向量分类机[J].智能系统学报,2018,13(1):147-151.[doi:10.11992/tis.201610019]
CHEN Xiaohua,LIU Dalian,TIAN Yingjie,et al.Extension support vector classification machine[J].CAAI Transactions on Intelligent Systems,2018,13(1):147-151.[doi:10.11992/tis.201610019]
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CHEN Xiaohua,LIU Dalian,TIAN Yingjie,et al.Extension support vector classification machine[J].CAAI Transactions on Intelligent Systems,2018,13(1):147-151.[doi:10.11992/tis.201610019]
可拓支持向量分类机
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
13
期数:
2018年第1期
页码:
147-151
栏目:
学术论文—智能系统
出版日期:
2018-01-24
- Title:
- Extension support vector classification machine
- Keywords:
- data mining; extension; classification; support vector machine; optimization; kernel function; prior knowledge; statistical learning theory
- 分类号:
- TP181
- 摘要:
- 针对分类问题，基于可拓学的思想，提出了可拓支持向量分类机算法。与标准的支持向量分类机不同，可拓支持向量机在进行分类预测的同时，更注重于找到那些通过变化特征值而转换类别的样本。文中给出了可拓变量和可拓分类问题的定义，并构建了求解可拓分类问题的两种可拓支持向量机算法。把可拓学与SVM结合是一种新的方向，文中所提出的算法还有待进一步的理论分析，将在未来的工作里，继续探索如何在可拓学的基础上，构建更加完善的可拓SVM方法。
- Abstract:
- We propose an extension support vector machine (ESVM) to address the classification problem. Unlike the standard support vector machine, ESVM considers samples that can be converted into different labels by changing some feature values. We define the extension variables and extension classification problems and construct the corresponding optimization problem using a heuristic algorithm. In the future, we will improve the proposed method to incorporate the extension theory.
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备注/Memo
收稿日期:2016-10-15。
基金项目:国家自然科学基金项目（61472390，11271361，71331005）；北京市自然科学基金项目（1162005）.
作者简介:陈晓华,女,1975年生,工程师,主要研究方向为电力系统及其自动化;刘大莲,女,1978年生,副教授,博士研究生,主要研究方向为数据挖掘;田英杰,男,1973年,研究员,博士生导师,主要研究方向为最优化与机器学习。
通讯作者:刘大莲.E-mail:ldlluck@sina.com.
更新日期/Last Update:
2018-02-01