在此，研究了几种不同的分类器集成方法。
设计了一种基于主成分分析的分类器集成方法。
A classifiers ensemble approach based on Principal Component Analysis (PCA) was proposed.
差异性是提高分类器集成泛化性能的重要因素。
Diversity among base classifiers is known to be an important factor for improving generalization performance in ensemble learning.
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.
给出了基于隶属度矩阵的模糊积分密度确定方法，介绍了基于模糊积分的分类器集成算法。
For this object, a method of determining fuzzy integral density with membership matrix is proposed, and the classifier ensemble algorithm based on fuzzy integral is introduced.
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.
为了改善网络性能，采用自适应警戒参数的方法对网络进行优化；采用多SFAM分类器集成复合的方法克服分类缺陷。
In order to improve network performance, the adaptive alert parameters was applied to optimize the network, and Multi-SFAM compounded classifier was used to overcome the classification defect.
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.
特征量被预处理后，输入到集成bP神经网络分类器中分类。
Finally, the features are preprocessed, then classified by integrating BP neural networks.
本文实现了基于分类器判决可靠度估计的最优线性集成方法。
This text realizes Optimal Linear Combination method basing on recognition confidence.
在性能测试与实际应用中，集成分类器均取得了良好的效果。
Applying the new integrated classifier, the results is satisfying not only in the performance test but also in the practical applications.
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.
集成学习方法通过同时构造多个学习器，然后对各学习器的分类结果使用投票法得到分类结果。
Integrated learning method can get classification results by constructing many learners to make a vote on classification results.
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.
该方法将高维分类器空间压缩至低维分类器空间，并在该空间内学习集成器。
It can reduce classifier space with high dimension, and then learn a combiner in lower dimension.
提出了一个基于集成学习方法的医学图像分类器。
The paper proposes a medical image classification based on ensemble leaning.
一个好的集成学习算法，关键是能生成差异度大的个体分类器。
The key of a good ensemble learning algorithm is able to generate the diversity of individual learners.
然后对脱机手写体汉字识别中常用的分类器以及集成方法进行了认真的学习和总结。
Then makes a summary of various classifiers and ensemble methods commonly used in the off-line handwritten Chinese characters recognition.
由于Q统计量在实验中效果不错，因此本文采用Q统计量度量两个分类器之间的差异度。提出一种采用Q统计量的选择性集成学习算法。
As the Q statistic is better in experiments, so this paper uses Q statistic to measure the diversity between the two learners and proposes a new selective ensemble algorithm basing Q statistic.
该方法把经单独训练的具有一定差异度的单个BP神经网络加以集成，构成舌苔分类器。
In the method, those single BP neural networks which have been trained solely are integrated averagely in order to build up the tongue coat classifier.
此外，与一般的贝叶斯集成分类器相比，PEBNC不必存储成员分类器的参数，空间复杂度大大降低。
Furthermore, compared with the general Bayesian classifier ensemble, PEBNC requires less space because there is no need to save parameters of individual classifiers.
选择性集成分类算法虽能提高集合分类器在整体数据集上的分类性能，但针对某一具体数据进行分类时，其选择出的个体分类器集合并不一定是最优组合。
Selective ensemble classifiers can improve classification accuracy rate of data set. But for a specific data classification, the classifiers contained by ensemble can not be the best combination.
选择性集成分类算法虽能提高集合分类器在整体数据集上的分类性能，但针对某一具体数据进行分类时，其选择出的个体分类器集合并不一定是最优组合。
Selective ensemble classifiers can improve classification accuracy rate of data set. But for a specific data classification, the classifiers contained by ensemble can not be the best combination.
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