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中文摘要：
进一步讨论了拟概率的一些性质，给出了拟概率空间上的拟随机变量及其分布函数、期望和方差的概念及若干性质；证明了拟概率空间上的Markov不等式、Chebyshev不等式和Khinchine大数定律；给出并证明了拟概率空间上学习理论的关键定理和学习过程一致收敛速度的界，把概率空间上的学习理论的关键定理和学习过程一致收敛速度的界推广到了拟概率空间，为系统地建立拟概率上的统计学习理论与构建支持向量机奠定了理论基础．
英文摘要：
Some properties of quasi-probability are further discussed. The definitions and properties of quasi-random variable and its distribution function, expected value and variance are then presented. Markov inequality, Chebyshev＇s inequality and the Khinchine＇s law of large numbers on quasi-probability spaces are also proved. Then the key theorem of learning theory on quasiprobability spaces is proved, and the bounds on the rate of uniform convergence of learning process on quasi-probability spaces are constructed. The investigations will help lay essential theoretical foundations for the systematic and comprehensive development of the quasi-statistical learning theory.
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