| Fisher和支持向量的综合分类器 |
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| 引用本文： | 安文娟,孙德山.Fisher和支持向量的综合分类器[J].计算机工程与应用,2010,46(29):183-185. |
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| 作者姓名： | 安文娟 孙德山 |
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| 作者单位： | 辽宁师范大学,数学学院,辽宁,大连,116029 |
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| 基金项目： | 辽宁省高等学校科研项目 |
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| 摘 要： | 结合Fisher判别分析和支持向量机的优点，提出了一种新的分类算法—Fisher-SV分类器（简称FSVC）。该分类器的核心思想就是寻找最优分类面的法向量w*，使得样本向量在w*上做投影后，不仅使分类间隔达到最大，而且使类内离散程度尽可能小。对于线性情况，可以转化为传统的支持向量机求解，而不需要设计新的求解算法。对于非线性情况，利用再生核理论得出新的求解算法。实验结果表明，该分类器具有很高的准确度和可靠性。
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| 关 键 词： | Fisher判别 支持向量机 分类间隔 类内离散程度 再生核理论 |
| 收稿时间： | 2009-3-9 |
| 修稿时间： | 2009-5-5
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Fisher-support vector classifier |
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| AN Wen-juan,SUN De-shan.Fisher-support vector classifier[J].Computer Engineering and Applications,2010,46(29):183-185. |
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| Authors: | AN Wen-juan SUN De-shan |
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| Affiliation: | (Institute of Mathematics,Liaoning Normal University,Dalian,Liaoning 116029,China) |
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| Abstract: | With combination of advantages of both Fisher discriminant analysis and support vector machines,this paper develops an improved classification algorithm,called Fisher-Support Vector Classifier.The central idea is that the vector w^＊ of the optimal hyperplane is found along which the samples are projected such that the margin is maximized while within-class scatter is kept as small as possible.In linear case,it can be converted to traditional Support Vector Machines（SVM） to solve and doesn＇t need to design new algorithms.In nonlinear case, a new algorithm is produced by the reproducing kernel theory. The test result shows that the Fisher-Support Vector Classifier established has a high accuracy and reliability. |
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| Keywords: | Fisher discrimination support vector machines margin within-class scatter reproducing kernel theory |
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