This paper studies the design of pattern recognition system based multiple classifiers combination.
本文对多分类器融合模式识别的设计方法进行了研究。
The methods of multiple classifiers combination are proposed to classify protein-protein interaction sites.
提出将多分类器组合算法应用于蛋白质-蛋白质相互作用位点预测。
There are two strategies for multiple classifiers combination: multiple classifiers fusion and multiple classifiers selection.
多分类器组合策略有两类：多分类器融合和多分类器选择。
At the same time this paper is also a beneficial trying on the application of classifiers combination technology in the traffic prediction field.
同时也是将分类器组合技术应用到交通预测领域的有益尝试。
Multiple classifiers combination makes use of the complementarities of different classifiers and different characters to improve recognition correctness.
多分类器组合利用不同分类器、不同特征之间的互补性，提高了组合分类器的识别率。
Based on thought of multiple classifiers combination method, this paper proposes a combination classification method of multiple decision trees based on PSO Algorithm.
针对数据挖掘中的分类问题，依据组合分类方法的思想，提出一种基于遗传算法的多重决策树组合分类方法。
The combination of multiple classifiers is one of the effective ways to improve the recognition performance.
多分类器组合是提高识别效果的一条有效途径。
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.
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。
A new method of speech emotion recognition via voting combination of multiple classifiers is proposed for improving speech emotion classification rate.
为了提高语音情感的正确识别率，提出一种基于多分类器投票组合的语音情感识别新方法。
In a wide range of applications, the combination of classifiers leads to substantial reduction of misclassification error.
在很多应用中，组合使用多个分类器可以降低分类错误率。
Absrtact: By considering the error rates and the training speed of neural networks, a hierarchical classifiers which is called as BP - LVQ neural network combination model is proposed in this paper.
摘要：综合考虑神经网络分类误差率以及训练速率，文中从组合分类器结构出发，提出一种树形多层的BP—LV Q神经网络组合分类器模型。
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.
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。
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