| 摘要: |
| 介绍机器学习的表示方式,分析和比较机器学习中经验风险最小化原则和结构风险最小化原则,引出用于回归估计的支持向量机,并用数学方式阐述其基本思想,讨论支持向量机技术发展中存在的主要问题. |
| 关键词: 支持向量机 回归估计 经验风险最小化 结构风险最小化 |
| DOI： |
| 投稿时间：2005-07-05 |
| 基金项目: |
|
| The Support Vector Machines for Regression |
|
Li Zhiming, Kong Lingfu
|
| (Coll. of Info. Sci. and Engi., Yanshan Univ., Qinhuangdao, Hebei, 066004, China) |
| Abstract: |
| The expression of machine learning is introduced.The empirical risk minimization and the structural risk minimization in machine learning are analyzed.A support vector machine for regression is presented.The basic idea an dthe main issues in the development of the support vector machine are discussed. |
| Key words: support vector machine regression empirical risk minimization structural risk minimization |
