基于混合Hausdorff距离的多示例学习近邻分类器

doi:10.6040/j.issn.1672-3961.1.2016.019

山东大学学报(工学版) ›› 2016, Vol. 46 ›› Issue (6): 15-22.doi: 10.6040/j.issn.1672-3961.1.2016.019

基于混合Hausdorff距离的多示例学习近邻分类器

陈泽华,尚晓慧,柴晶

太原理工大学信息工程学院, 山西太原 030024

收稿日期:2016-07-12 出版日期:2016-12-20 发布日期:2016-07-12
通讯作者: 柴晶(1983— ),男,山东聊城人,讲师,博士,主要研究方向为数据挖掘和机器学习.E-mail:jingchai@aliyun.com E-mail:zehuachen@163.com
作者简介:陈泽华(1974— ),女,山西太原人,副教授,博士,主要研究方向为智能信息处理.E-mail:zehuachen@163.com
基金资助:
国家自然科学基金资助项目(61403273,61402319);山西省自然科学基金资助项目(2014021022-4,2014021022-3)

Neighborhood related multiple-instance classifiers based on integrated Hausdorff distance

CHEN Zehua, SHANG Xiaohui, CHAI Jing

College of Information Engineering, Taiyuan University of Technology, Taiyuan 030024, Shanxi, China

Received:2016-07-12 Online:2016-12-20 Published:2016-07-12

摘要/Abstract

摘要： 通过对最小和最大Hausdorff距离的分析,提出混合Hausdorff距离将它们融合在一起以弥补任意单一Hausdorff距离的缺陷,并基于混合Hausdorff距离设计多示例学习近邻分类器。采用近邻分量分析模型能够优化混合Hausdorff距离中的权系数,从而得到在近邻分类准则下最优的混合Hausdorff距离。结果表明:相对于任意单一Hausdorff距离,基于混合Hausdorff距离的多示例学习近邻分类器通常能够获得更高的识别精度。

关键词: Hausdorff距离, 多示例学习, 近邻分量分析, 分类器, 权系数

Abstract: Based on the analysis of minimal Hausdorff(minH)and maximal Hausdorff(maxH)distances, integrated Hausdorff(intH)distance was proposed to combine minH and maxH, and used to design neighborhood related multiple-instance classifiers. The Neighborhood Component Analysis(NCA)model was used to learn the weighting coefficients in intH automatically and obtain the optimal intH according to the neighborhood related classification criterion. The experimental results showed that in most cases, compared with minH and maxH, intH could improve the classification accuracies of neighborhood related multiple-instance classifiers.

Key words: multiple-instance learning, weighting coefficients, Hausdorff distance, classifier, neighborhood component analysis

中图分类号:

TP181

陈泽华,尚晓慧,柴晶. 基于混合Hausdorff距离的多示例学习近邻分类器[J]. 山东大学学报(工学版), 2016, 46(6): 15-22.

CHEN Zehua, SHANG Xiaohui, CHAI Jing. Neighborhood related multiple-instance classifiers based on integrated Hausdorff distance[J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2016, 46(6): 15-22.

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基于混合Hausdorff距离的多示例学习近邻分类器

Neighborhood related multiple-instance classifiers based on integrated Hausdorff distance

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