在车牌字符识别中引入了误识模型和多分类器集成技术。
The techniques of mis-recognition model and multiple classifier combination are proposed and used in the system.
多分类器集成系统是当前机器学习领域的一个研究热点。
Integration of multiple classifier machine learning system is currently a hot research field.
本文提出一种联机识别自然手写体汉字的多分类器集成模型。
In the paper, a new multiple classifiers integrated model of online recognizing natural handwritten Chinese character is presented.
本文在理解和分析各种分类器以及分类器集成方法的基础上提出了一种新的多分类器集成的方法。
After comprehending and analyzing the various classifiers and integration of multi-classifiers, a new method of multi-classifier ensemble is presented in this paper.
然后，利用证据组合规则对多分类器进行集成。
Then, all the classification results are integrated by the use of D-S combination rule.
分类器选择是一种设计多分类器系统的有效方法，从给定候选分类器集中挑选出一个子集，使得该子集集成性能最佳。
The goal of classifier selection is to select a subset of classifiers from a given set of candidate classifiers, to achieve the best combination performance.
为改善多分类器系统的分类性能，提出了基于广义粗集的集成特征选择方法。
For improving the performance of multiple classifier system, a novel method of ensemble feature selection is proposed based on generalized rough set.
为改善多分类器系统的分类性能，提出了基于广义粗集的集成特征选择方法。
For improving the performance of multiple classifier system, a novel method of ensemble feature selection is proposed based on generalized rough set.
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