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| 基于支持向量机分类的回归方法 |
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陶卿1,2, 曹进德3, 孙德敏4
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1.中国科学院,自动化研究所,北京,100080;2.中国人民解放军炮兵学院,一系,安徽,合肥,230031;3.东南大学,应用数学系,江苏,南京,210096;4.中国科学技术大学,自动化系,安徽,合肥,230027
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| 摘要: |
| 支持向量机(support vector machine,简称SVM)是一种基于结构风险最小化原理的分类技术,也是一种新的具有很好泛化性能的回归方法.提出了一种将回归问题转化为分类问题的新思想.这种方法具有一定的理论依据,与SVM回归算法相比,其优化问题几何意义清楚明确. |
| 关键词: 回归 分类 支持向量 最大边缘 |
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| 基金项目:国家自然科学基金资助项目(60175023);中国博士后科学基金资助项目(5030436);安徽省自然科学基金资助项目(01042304);安徽省优秀青年基金资助项目 |
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| A Regression Method Based on the Support Vectors for Classification |
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TAO Qing,CAO Jin-de,SUN De-min
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| Abstract: |
| The support vector machine is a classification technique based on the structural risk minimization principle, and it is also a class of regression method with good generalization ability. In this paper, a new idea that each regression problem can be changed into a classification problem is presented. The proposed method has some theoretical foundations. Compared with SVM regression method, the geometric meaning of optimization problem in this paper is very clear and obvious. |
| Key words: regression classification support vector machines maximal margin |
