uniform bounds

基本解釋

This paper mainly investigates the bounds on the rate of uniform convergence of the fuzzy learning processes.
本文給出了非一致收斂的幾個定理，并以較多的實(shí)例說明它們的應(yīng)用。
This article deals with error bounds for linear congruential sequences and vectors which are used as pseudorandom numbers and vectors of uniform distribution.
采用關(guān)于網(wǎng)格理論的方法，對線性同余序列及向量列在其作為偽隨機(jī)序列模擬均勻分布時的偏差加以討論并給出估計。
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.
最后證明基于隨機(jī)粗糙樣本的統(tǒng)計學(xué)習(xí)理論的關(guān)鍵定理并討論學(xué)習(xí)過程一致收斂速度的界。