Multiple classifier fusion in probabilistic neural networks

作     者：Grim, J Kittler, J Pudil, P Somol, P 

作者机构：Acad Sci Czech Republic Inst Informat Theory & Automat CZ-18208 Prague Czech Republic Univ Surrey Sch Elect Engn Informat Technol & Math Guildford GU2 5XH Surrey England 

出 版 物：《PATTERN ANALYSIS AND APPLICATIONS》 (模式分析与应用)

年 卷 期：2002年第5卷第2期

页      面：221-233页

核心收录：

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：EM algorithm information preserving transform multiple classifier fusion probabilistic neural networks structure optimisation subspace approach 

摘      要：The main motivation of this paper is to design a statistically well justified and biologically compatible neural network model and, in particular, to suggest a theoretical interpretation of the well known high parallelism of biological neural networks. We consider a novel probabilistic approach to neural networks developed in the framework of statistical pattern recognition, and refer to a series of theoretical results published earlier. It is shown that the proposed parallel fusion of probabilistic neural networks produces biologically plausible structures and improves the resulting recognition performance. The complete design methodology based on the EM algorithm has been applied to recognise unconstrained handwritten numerals from the database of Concordia University Montreal. We achieved a recognition accuracy of about 95%, which is comparable with other published results.