多分类器组合是对决策层的数据进行融合。
The multiple classifiers combination fuses the decision level data.
多分类器组合是提高识别效果的一条有效途径。
The combination of multiple classifiers is one of the effective ways to improve the recognition performance.
多分类器组合是解决复杂模式识别问题的有效办法。
Multiple classifiers ensemble is an effective method to solve complex classification problems in pattern recognition field.
多分类器组合策略有两类：多分类器融合和多分类器选择。
There are two strategies for multiple classifiers combination: multiple classifiers fusion and multiple classifiers selection.
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。
The methods of multiple classifiers combination are proposed to classify protein-protein interaction sites.
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。
Multiple classifiers combination makes use of the complementarities of different classifiers and different characters to improve recognition correctness.
因此，进行多分类器组合研究，探讨其在遥感影像自动分类中的应用，具有重要的理论与实践意义。
Therefore, it is theoretically and practically significant to study the method of combining multiple classifiers and explore its application in automatic classification of remote sensing images.
多分类器组合方法可以在一定程度上弥补单个分类器的不足，提高分类性能，因此，它在模式识别领域得到广泛的应用。
Since multiple classifier systems(MCS) can improve the performance of classification, the technique has been widely used in various fields of pattern recognition.
多分类器组合的目的是希望能够充分发挥每个分类器在各自分类性能上的长处，以获得比任何单独分类器都要高的识别率。
The objective of multi-classifier combination is to make use of each classifier"s good qualities in recognition performance and gains higher recognition rate than each classifier."
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。
A new method of speech emotion recognition via voting combination of multiple classifiers is proposed for improving speech emotion classification rate.
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.
实验结果表明：利用多特征组合多分类器的方法可以提高“文本无关”说话人辨认系统的识别率和可靠性。
The experimental results have shown that Combining Multiple Classifiers with different features can result in satisfactory and significant improvement in recognition performance.
组合多分类器可以看作是一种用于获得较高识别效果的混合系统。
Combining Multiple Classifiers can be viewed as a novel hybrid system to achieve high recognition accuracy for Text Independent Speaker Identification.
针对标准数据集在评估多分类器系统的组合方法时存在的不足，设计了一种新的分类器模拟算法。
Aiming at the deficiency of evaluating classifier combination methods with standard data sets, a new classifier simulation algorithm was proposed.
重点探索了以不同特征作为输入的组合多分类器方法。
This article has summarized current methods of combining multiple classifiers, and investigated on embodying different features as input vectors.
重点探索了以不同特征作为输入的组合多分类器方法。
This article has summarized current methods of combining multiple classifiers, and investigated on embodying different features as input vectors.
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