关于算法分析的定理证明了这种混合算法对于紧致集内的权向量构成的任意连续函数能依概率1收敛于全局极小值。

It is shown that this algorithm ensures convergence to a global minimum with probability 1in a compact region of a weight vector space.

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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。

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.

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RASVM具有更少的收敛的支持向量、训练时间和更高的预测精度和泛化能力。

RASVM possesses less convergence support vectors, training time and higher precision of prediction, capacity of generalization.

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