【摘要】 文中提出了基于后验概率估计的多特征多分类器组合识别的估计法，并提出了基于具有统计不相关性的最佳鉴别变换与ＫＬ变换抽取手写体数字的有效鉴别特征的方法．实验采用Ｃｏｎｃｏｒｄｉａ Ｕｎｉｖｅｒｓｉｔｙ ＣＥＮＰＡＲＭＩ手写体数字数据库．用最近邻距离分类器与最近邻相关分类器这两个分类器，对手写体数字的１２ 个特征做多特征多分类器组合识别实验． 实验结果表明：估计法优于常用的投票法与计分法，估计法的识别率高达９７％ ．本文最后基于一个严格的结构分类器与估计法提出了一个集成分类器，该集成分类器获得了更好的实验结果：识别率、拒识率与可靠性分别可达到９７．１５％ 、２．０５％ 、９９．１８％ ，这是目前在该手写体数字数据库上所得到的最好的实验结果．更多还原

【Abstract】 Presented in this paper is an estimating method of multi\|feature and multi\|classifier combination based on the posterior probability estimators. Also presented is a method to extract effective discriminant features of handwritten digits based on a set of uncorrelated optimal discriminant features and KL transform. Experiments have been performed with Concordia University CENPARMI’s handwritten digit database based on the nearest\|neighbor distance classifier and the nearest\|neighbor correlation classifier, and 12 features of handwritten digits. Experimental results show that the estimating method is better than the polling method or the counting method respectively and the recognition rate of the estimating method is as high as 97%. A new combination classifier is finally brought forward, which is based on the strict structure classifier and the estimating method. Better experimental results have been obtained by means of this new combination classifier: the recognition rate, the reject rate, and the reliability are as high as 97.15%, 2.05%, and 99.18% respectively, which are the best results up to now on the handwritten digit database.更多还原