您当前所在位置: 首页 > 学者

胡清华

  • 19浏览

  • 0点赞

  • 0收藏

  • 0分享

  • 1下载

  • 0评论

  • 引用

期刊论文

Improved support vector machine algorithm for heterogeneous data

暂无

Pattern Recognition,2015,48(6):2072-2083 | 2015年06月01日 | doi.org/10.1016/j.patcog.2014.12.015

URL:https://www.sciencedirect.com/science/article/abs/pii/S0031320314005263

摘要/描述

A support vector machine (SVM) is a popular algorithm for classification learning. The classical SVM effectively manages classification tasks defined by means of numerical attributes. However, both numerical and nominal attributes are used in practical tasks and the classical SVM does not fully consider the difference between them. Nominal attributes are usually regarded as numerical after coding. This may deteriorate the performance of learning algorithms. In this study, we propose a novel SVM algorithm for learning with heterogeneous data, known as a heterogeneous SVM (HSVM). The proposed algorithm learns an mapping to embed nominal attributes into a real space by minimizing an estimated generalization error, instead of by direct coding. Extensive experiments are conducted, and some interesting results are obtained. The experiments show that HSVM improves classification performance for both nominal and heterogeneous data.

【免责声明】以下全部内容由[胡清华]上传于[2020年11月11日 16时20分11秒],版权归原创者所有。本文仅代表作者本人观点,与本网站无关。本网站对文中陈述、观点判断保持中立,不对所包含内容的准确性、可靠性或完整性提供任何明示或暗示的保证。请读者仅作参考,并请自行承担全部责任。

我要评论

全部评论 0

本学者其他成果

    同领域成果