Finally the key theorem of statistical learning theory based on random rough samples is proved, and the bounds on the rate of uniform convergence of learning process are discussed.
最后证明基于双重随机样本的统计学习理论的关键定理并讨论学习过程一致收敛速度的界。
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。
The simulation and motor control show that the new algorithm has fast learning rate, good convergence properties and can overcome the defects of traditional PID algorithm.
仿真实验及在伺服电机转速控制中的应用表明，该算法具有较快的学习速度及良好的收敛性能，并有效地克服了传统PID算法的缺陷。
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