一种改进的推广能力衡量准则

通过对支撑向量机推广能力的分析，提出了一种构造性的与样本分布有关的推广能力衡量准则．该准则与统计学习理论中的推广能力准则具有几何上的一致性，由样本的二阶统计量构成，比已有的完全不依赖于样本分布的推广能力上界更能反映学习过程的收敛性和收敛速率．较为重要的一点是该准则在学习过程之前是可处理的，所以它可以用作所有分类器中数据预处理的准则，同时也可以为支撑向量机模型的选择提供依据．文中最后给出的人工及实际的例子也很好地说明了该准则的合理性．

An Improved Principle for Measuring Generalization Performance

A new constructive principle, which depends on the distribution of examples, for measuring the generalization performance is proposed based on the analysis of the generalization performance of support vector machines. The principle is consistent in geometry with that in statistical learning theory, composed of two-order statistic of samples and shows the convergence rate of learning process well. It is important that this new principle can be processed before learning. So this new principle can be taken as a rule for all classifiers to preprocess data and to select model for SVMs.Simulation results for both artificial and real data show the rationality of this principle.