Support vector machine (SVM) is a new general learning machine, which analyzes the consistency of learning and speed of convergence from structure risk minimization principle.

支持向量机(SVM)是一种新的通用学习机器，它从结构风险最小化的角度，分析了学习过程的一致性、收敛速度等。

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Structural risk minimization induce principle is used to control the bound on the value of achieved risk by controlling experiential risk and belief bound at the same time.

结构风险最小化归纳原则通过控制经验风险和置信范围来控制实际风险的界。

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Based on the principle of construction risk minimization, the relations among the main variants are found out to yield a general rule which is then used to obtain the accurate optimizations.

根据结构风险最小化原则，在“数据有限”的情况下，找到各种主要变量之间的关系，从复杂系统中归纳出一般规律，进而准确得到优化结果。

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