[1]李欢,王士同.支持向量机的多观测样本二分类算法[J].智能系统学报,2014,9(04):392-400.[doi:10.3969/j.issn.1673-4785.201312040]
 LI Huan,WANG Shitong.Binary-class classification algorithm with multiple-access acquired objects based on the SVM[J].CAAI Transactions on Intelligent Systems,2014,9(04):392-400.[doi:10.3969/j.issn.1673-4785.201312040]
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《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
第9卷
期数:
2014年04期
页码:
392-400
栏目:
出版日期:
2014-08-25

文章信息/Info

Title:
Binary-class classification algorithm with multiple-access acquired objects based on the SVM
作者:
李欢 王士同
江南大学 数字媒体学院, 江苏 无锡 214000
Author(s):
LI Huan WANG Shitong
1. School of Digital Media, Jiangnan University, Wuxi 214000, China;
2. School of Digital Media, Jiangnan University, Wuxi 214000, China
关键词:
模式识别多观测同类样本SVM二分类
Keywords:
pattern recognitionmultiple observationssimilar samplesSVMbinary-class classification
分类号:
TP391.4
DOI:
10.3969/j.issn.1673-4785.201312040
摘要:
针对多观测样本的分类问题, 提出基于SVM的多观测样本二分类算法。每次分类时, 首先限制组成多观测样本的所有单观测样本属于同一类别, 对多观测样本的类别做2次假设, 通过比较不同类别假设下的目标函数最优解来确定多观测样本的类别。该方法无需对分类器进行训练或提前对训练集进行特征表示, 而是将已知标签样本集和多观测样本作为一个整体, 充分利用特征空间中同类样本连续分布这一特点, 使得分类更加准确。结果表明所提方法的有效性。
Abstract:
The binary-class classification algorithm with multiple-access acquired objects based on the SVM is proposed for the purpose of classification of an object given with multiple observations in this paper. In each classification, initially all single observation samples in the multiple observation sample set are restricted to a same class.Two hypotheses are made for the class of the multiple observation sample set, and the class is determined by comparing the optimal values of the different objective functions under different class hypotheses. This method does not require training the classifier or early feature representation of the training set, instead, it takes advantage of the continuity law of the feature space of similar samples with the labeled samples and multiple observation samples as a whole, making the algorithm more accurate for classifications. Experiments show that the proposed method is valid and efficient.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期:2013-12-20。
基金项目:国家自然科学基金资助项目(61272210);江苏省自然科学基金资助项目(BK2011417, BK2011003);江苏省"333"工程基金资助项目(BRA2011142)
作者简介:王士同,男,1964年生,教授,博士生导师,主要研究方向为人工智能、模式识别和生物信息。
通讯作者:李欢,男,1990年生,硕士研究生,主要研究方向为人工智能、模式识别。E-mail:huanli1130@126.com
更新日期/Last Update: 1900-01-01