[1]赵春晖,张燚.相关向量机分类方法的研究进展与分析[J].智能系统学报,2012,7(04):294-301.
 ZHAO Chunhui,ZHANG Yi.Research progress and analysis on methods for classification of RVM[J].CAAI Transactions on Intelligent Systems,2012,7(04):294-301.
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《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
第7卷
期数:
2012年04期
页码:
294-301
栏目:
出版日期:
2012-08-25

文章信息/Info

Title:
Research progress and analysis on  methods for classification of RVM
文章编号:
1673-4785(2012)04-0294-08
作者:
赵春晖 张燚
哈尔滨工程大学 信息与通信工程学院,黑龙江 哈尔滨 150001
Author(s):
ZHAO Chunhui ZHANG Yi
College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001,China
关键词:
相关向量机改进型相关向量机高光谱图像分类算法
Keywords:
relevance vector machineimproved relevance vector machinehyperspectral imageclassification algorithm
分类号:
TP751.1
文献标志码:
A
摘要:
相关向量机(RVM)是一种基于贝叶斯模型的监督机器学习算法,可用于处理回归以及分类问题.与支持向量机(SVM)相比,相关向量机的优点在于其输出结果是一种概率模型,其相关向量的个数远远小于支持向量的个数,并且测试时间短.总结了相关向量机的基本原理及主要应用领域,详细阐述了相关向量机的模型结构以及分类方法,重点介绍了在高光谱图像分类中的应用.并针对RVM算法在高光谱图像分类中的不足,给出了多种改进方案,并作以比较.希望对研究者今后的研究有所启发,以促进该领域的发展.
Abstract:
The relevance vector machine (RVM) is a machine learning algorithm which is based on supervision of a Bayesian model. It can be used to deal with regression and classification problems. Compared with the support vector machine (SVM), the relevance vector machine has the advantage that its output is a probability model and the number of relevance vectors is far fewer than the number of support vectors. In this paper, the application was summarized with a relevance vector machine and the classification of a hyperspectral image with RVM was introduced; the RVM model and the method of classification were also explained. In light of the disadvantage of classification, some improved methods were summarized. Various methods were generalized and analyzed while attempting to find breakthroughs and promote further research.

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备注/Memo

备注/Memo:
收稿日期: 2011-12-30.
网络出版日期:2012-07-12.
基金项目:国家自然科学基金资助项目(61077079);教育部博士点基金资助项目 (20102304110013). 
通信作者:赵春晖.
E-mail:zhaochunhui@hrbeu.edu.cn.
作者简介:
赵春晖,男,1965年生,教授、博士生导师.主要研究方向为智能信息处理、图像处理.获省部级科技奖5项,出版著作3部,发表学术论文430余篇.
张燚,男,1987年生,硕士研究生.主要研究方向为高光谱图像分类技术,发表学术论文5篇.
更新日期/Last Update: 2012-09-26