| 摘要: |
| 针对支持向量机应用于函数估计时支持向量过多所引起的计算复杂性,提出一种简化算法,可以大幅度地减少支持向量的数量,从而简化其应用.采用简化算法还可以将最小平方支持向量机算法和串行最小化算法结合起来,达到学习效率高且生成的支持向量少的效果. |
| 关键词: 支持向量机 回归 机器学习 计算复杂性 算法 |
| DOI： |
| 分类号: |
| 基金项目:国家铁道部科技研究开发项目(2000X030-A) |
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| A Simplification Algorithm to Support Vector Machines for Regression |
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TIAN Sheng-feng,HUANG Hou-kuan
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| Abstract: |
| Aiming at the computational complexity resulted from the large amounts of support vectors when the support vector machines (SVMs) are used in function estimation, a simplification algorithm is presented to reduce the number of support vectors and simplify applications. By the adaptation of the simplification algorithm, the LS-SVM (least square support vector machine) algorithm can be combined with SMO (sequential minimal optimization) algorithm to achieve good results with high learning efficiency and a few number of support vectors. |
| Key words: support vector machine regression machine learning computational complexity algorithm |
