中文摘要：

最小最大风险准则判决是在类先验概率未知情况下的一个重要的决策方法，由该方法设计的分类器在大多数情况下存在性能下降过多的问题．为了提高分类器的分类性能，本文提出一种基于分段线性化思想的分类器设计方法．该方法首先对类的先验概率做一粗略估计，然后判断它所处的概率区间，最后用该区间相对应的分类器进行判决．理论推导和实验结果表明，该方法是一种有效方法，据此设计的分类器性能接近于贝叶斯分类器．

英文摘要：

The minimax risk criterion based decision is an important method for making decisions when priori probabilities are unknown. However, the performance of a minimax risk criterion based classifier is poor in most cases. To improve the performance of the designed classifier, a piecewise linearization based design method is presented. Firstly, the proposed method makes a rough estimation of the prior probability. Then, it decides the right interval where the estimated prior lies. Finally, the corresponding classifier is employed to make a decision. The theoretical deduction and experimental results show that the presented method is efficient and the performance of the corresponding classifier designed by the method approaches to Bayesian classifier.