In this paper, the bounds on the rate of uniform convergence of the learning processes on possibility space are discussed based on the classic Statistical learning Theory.

本文在经典统计学习理论的基础上，讨论了可能性空间上学习过程一致收敛速度的界。

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This article deals with error bounds for linear congruential sequences and vectors which are used as pseudorandom Numbers and vectors of uniform distribution.

采用关于网格理论的方法，对线性同余序列及向量列在其作为伪随机序列模拟均匀分布时的偏差加以讨论并给出估计。

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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。

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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。

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