| 摘要: |
| 目前的支持向量集约简法在寻找约简向量的过程中需要求解一个无约束的多参数优化问题,这样,像其他非线性优化问题一样,求解过程需要面对数值不稳定或局部最小值问题.为此,提出了一种基于核聚类的SVM(support vector machine)简化方法.此方法首先在特征空间中对支持向量进行聚类,然后寻找特征空间中的聚类中心在输入空间中的原像以形成约简向量集.该方法概念简单,在简化过程中只需求解线性代数问题,从而解决了现存方法存在的瓶颈问题.实验结果表明,该简化法能够在基本保持SVM泛化性能的情况下极大地约简支持向量,从而提高SVM的分类速度. |
| 关键词: 支持向量机 约简向量集 核聚类 原像 最佳权值 |
| DOI： |
| 分类号: |
| 基金项目:Supported by the National Basic Research Program of China under Grant No.2003CB317000 (国家重点基础研究发展计划(973)) |
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| Simplified Support Vector Machine Based on Reduced Vector Set Method |
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ZENG Zhi-Qiang,GAO Ji
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| Abstract: |
| Existing works of reduced support vector set method find the reduced set vectors based on solving an unconstrained optimization problem with multivariables,which may suffer from numerical instability or get trapped in a local minimum.In this paper,a reduced set method relying on kernel-based clustering is presented to simplify SVM(support vector machine)solution.The method firstly organizes support vectors in clusters in feature space, and then,it finds the pre-images of the cluster centroids in feature space to construct a reduced vector set.This approach is conceptually simpler,involves only linear algebra and overcomes the difficulties existing in the former reduced set methods.Experimental results on real data sets indicate that the proposed method is effective in simplifying SVM solution while preserving machine's generalization performance. |
| Key words: support vector machine reduced vector set kernel-based clustering pre-image optimal weight |
