The basic statistical learning theory (SLT) and its corresponding algorithms, support vector machines (SVMs), are surveyed, and especially, its latest research results are summarized and studied.

为了系统地归纳统计学习理论与支持向量机的基本思想和算法，总结目前该领域的最新研究成果。

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Support vector machine (SVM) is a novel and powerful learning method which is derived based on statistical learning theory (SLT) and the structural risk minimization principle.

建立在统计学习理论基础之上的支持向量机(SVM)，是一种基于结构风险最小的小样本机器学习方法。

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Support Vector Machines(SVM) are developed from the theory of limited samples Statistical Learning Theory (SLT) by Vapnik et al. , which are originally designed for binary classification.

支持向量机（SVM）是建立在统计学习理论基础上的一种小样本机器学习方法，用于解决二分类问题。

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