基于类条件置信变换的后验概率估计方法

A Novel Approach to Estimate Posterior Probabilities by Class Conditional Confidence Transformations

后验概率估计是模式识别多分类器组合方法研究的基础 ,该文提出了最近邻距离分类器后验概率估计的类条件置信变换方法 .后验概率被认为集中在最近邻类与次近邻类上 ,而且对每一个模式类 ,都有一个类条件置信变换函数 ,该函数可以通过实验数据估计得到 .实验采用Concordia大学CENPARMI手写体数字数据库与南京理工大学手写体数字数据库 .实验结果表明该文所提出的类条件置信变换方法是合理的 ,在降低识别错误率上 ,优于现有的投票法、记分法、线性法以及自适应置信变换 (ACT)法 .

It is a crucial problem to estimate posterior probabilities for combination techniques in pattern recognition. This paper proposes a novel class conditional confidence transformation approach to estimate posterior probabilities for 1NN distance classifiers. In the paper, posterior probabilities are assumed to be distributed on the nearest neighbor class and the second nearest neighbor class. It is supposed that there exists a class conditional confidence transformation function for each class. This paper gives an estimation approach to the class conditional confidence transformation functions by using training samples. Experiments have been performed with Concordia University CENPARMI’s handwritten digit database and Nanjing University of Science and Technology’s handwritten digit database. Experimental results show that there is a great deal of reason in the proposed approach to estimate class conditional confidence transformation functions and the novel approach is superior to poll, count, the linear approach and the adaptive confidence transform (ACT) approach in decreasing the erroneous classification rates.