| 关联向量机及其在入侵检测中的应用探讨 |
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| 引用本文： | 王帅伟,麦永浩,杨辉华,王勇.关联向量机及其在入侵检测中的应用探讨[J].成都信息工程学院学报,2005,20(3):270-274. |
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| 作者姓名： | 王帅伟 麦永浩 杨辉华 王勇 |
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| 作者单位： | 1. 桂林电子工业学院,广西,桂林,541004
2. 湖北警官学院,湖北,武汉,430034 |
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| 摘 要： | 介绍一种稀疏的贝叶斯学习算法——关联向量机(RVM)，它在再生核希尔伯特空间中学习，利用贝叶斯方法推理，推广能力好，与支持向量机相比不仅解更为稀疏而且不需要调整超参数。应用RVM的对小样本的良好分类能力，提出一种基于RVM的入侵检测原型系统。
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| 关 键 词： | 关联向量机 入侵检测 稀疏贝叶斯学习 |
| 文章编号： | 1671-1742(2005)03-0270-05 |
| 修稿时间： | 2004年9月2日 |
Discussion of RVM and its application in IDS |
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| WANG Shuai-wei,MAI Yong-hao,YANG Hui-hua,WANG Yong.Discussion of RVM and its application in IDS[J].Journal of Chengdu University of Information Technology,2005,20(3):270-274. |
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| Authors: | WANG Shuai-wei MAI Yong-hao YANG Hui-hua WANG Yong |
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| Abstract: | A general Bayesian framework for obtaining sparse solutions to the regression and classification tasks utilizing models linear in the parameters is presented. By using a probabilistic Bayesian learning framework an accurate RVM (relevance vector machine) prediction models using fewer basis functions than a comparable SVM (support vector machine) and offering a number of additional advantages is obtained. The generalization ability of the current ID (intrusion detection system) is poor with less priori knowledge. Using RVM in the intrusion detection the generalization ability of the IDS is good when the sample size is small. |
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| Keywords: | probabilistic Bayesian learning RVM IDS |
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