uniform bounds

基本解释

This paper mainly investigates the bounds on the rate of uniform convergence of the fuzzy learning processes.
本文给出了非一致收敛的几个定理，并以较多的实例说明它们的应用。
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。
Finally the key theorem of statistical learning theory based on birandom samples is proved, and the bounds on the rate of uniform convergence of learning process are discussed.
最后证明基于随机粗糙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。