| 局部支持向量机的研究进展 |
| |
| 引用本文： | 尹传环,牟少敏,田盛丰,黄厚宽,朱莹莹. 局部支持向量机的研究进展[J]. 计算机科学, 2012, 39(1): 170-174,189 |
| |
| 作者姓名： | 尹传环 牟少敏 田盛丰 黄厚宽 朱莹莹 |
| |
| 作者单位： | 1. 北京交通大学计算机与信息技术学院 北京100044
2. 山东农业大学信息科学与工程学院 泰安271018 |
| |
| 基金项目： | 国家自然科学基金,中央高校基本科研业务费专项资金,山东农业大学青年科技创新基金,北京交通大学科技基金 |
| |
| 摘 要： | 支持向量机是一种用途广泛的分类器,标准的支持向量机在预测每个样本点的类别时使用了训练集中所有的样本信息(即全局信息),然而这种全局化的方法并不蕴含一致性。局部支持向量机的提出符合"一致性蕴含局部性"的思路。首先回顾局部支持向量机的主要思想,然后阐述各种关于局部支持向量机的改进,并提出基于协同聚类的局部支持向量机用于大规模数据集,最后对局部支持向量机进行总结。
|
| 关 键 词： | 支持向量机 局部支持向量机 协同聚类 |
Survey of Recent Trends in Local Support Vector Machine |
| |
| YIN Chuan-huan , MU Shao-min , TIAN Sheng-feng , HUANG Hou-kuan , ZHU Ying-ying. Survey of Recent Trends in Local Support Vector Machine[J]. Computer Science, 2012, 39(1): 170-174,189 |
| |
| Authors: | YIN Chuan-huan MU Shao-min TIAN Sheng-feng HUANG Hou-kuan ZHU Ying-ying |
| |
| Affiliation: | 1(School of Computer and Information Technology,Beijing Jiaotong University,Beijing 100044,China)1(School of Computer and Information Engineering,Shandong Agriculture University,Taian 271018,China)2 |
| |
| Abstract: | Support Vector Machine(SVM) is an important and widely used classifier. If a sample wants to be classified,all training data will be used to obtain a hyperplane which is used to determine the label of that sample, that is, the SVM worked in global manner. However, this global behaviour doesn't imply consistency. hhe design of Local SVM(I_SVM)is in accordance with the result of"consistency implies local behaviour". In this paper, we first reviewed the main idea of LSVM, followed by the improvements on LSVM. In the following, we presented an LSVM algorithm which is based on cooperative clustering,reducing the time complexity of I_SVM in large scaled dataset. Then we ended this article by the conclusion. |
| |
| Keywords: | Support vector machine Local support vector machine Cooperative clustering |
| 本文献已被 CNKI 万方数据 等数据库收录！ |
|
点击此处可从《计算机科学》下载全文 |
|
