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.
结合组合式电脑茶叶拣梗机的模式分类问题，讨论了模式分类器智能化及实现策略。
The intelligent pattern classifier and its realization techniques are discussed according to the classing process in a computer control combined tea-stalk reject system.
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。
At the same time this paper is also a beneficial trying on the application of classifiers combination technology in the traffic prediction field.
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.
分类器组合的有效性问题以及最佳组合问题均需要解决。
The effectiveness of classifier combination and the problem of best combination both have to be solved.
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.
针对当前辐射源识别系统中存在的问题，提出了一种结合组合分类器技术的辐射源识别新方法。
Aiming at the problem of radar radiating source recognition system, we put forward a scheme applying combining classifier to radiating source recognition.
本文还进一步分析了组合分类器参数设置对该算法性能的影响。
The paper also presents a further discussion on the parameters of the combining classifier which will affect the performance.
实验显示，相比单纯的基于机器学习的分类系统，这种组合型分类器产生了8%的性能提升。
The experiment result shows that there is 8% performance improvement compared with the single classifying method based on machine learning.
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.
为提高少数类的分类性能，对基于数据预处理的组合分类器算法进行了研究。
In order to improve the performance of the minority class, a combined classifier algorithm is presented based on data pre - processing.
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.
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.
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.
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.
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.
快速的人脸检测还有一个重要任务就是将这些弱分类器有效地组合起来，提高人脸检测的速度。
Most effectively combing the weak classifiers is important work for increasing the speed of detecting task.
当子分类器均受训练样本分布影响较小，组合结果也具有较好的稳定性。
If the distribution of training samples only had little influence on the sub-classification, the combined classifiers would have stable performances.
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.
为了解决在没有已知标签样本的情况下数据流组合分类决策问题，提出一种基于约束学习的数据流组合分类器的融合策略。
To resolve combining classifiers decisions among ensemble classification over data streams without labeled examples, a transductive constraint-based learning strategy was proposed.
通过实际缺陷样本测试，组合分类器的识别精度达到92%以上，对单个分类器识别准确率的提高幅度大于10%。
Tests on real strip defects show that the recognition accuracy reaches above 92%, and improves the accuracy of single classifier up to 10%.
最后介绍和分析了分类器组合的方法，串行分类器组合算法的特异度和灵敏度离实际应用还有很大的差距。
The specificity and sensitivity of serial classifications combination is far from the practical application. Further analysis and improvement are needed.
组合分类结果受训练样本分布的影响取决于子分类器的稳定性。
It depends on the stability of sub-classification that whether the results of combined classification are affected by the distribution of training samples.
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.
气焊设备。焊接、切割和相关工艺用储气瓶上的组合流量计调节器。分类、规范和试验。
Gas welding equipment - Integrated flowmeter regulators used on cylinders for welding, cutting and allied processes - Classification, specification and tests.
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。
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.
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."
本文主要工作体现在瞬时参数的提取、模糊特征选择、单个分类器设计和组合分类器设计这四个方面。
The main contribution of this dissertation includes four aspects. They are instantaneous parameters extraction, fuzzy feature selection, single classifier design and combined classifier design.
实验结果表明，使用组合KFDA的方法预测的效果优于FDA和PCA以及单个KFDA分类器的预测效果，预测准确率为86.5%。
The results indicate that the performance of ensembles of KFDA is better than that of FDA, PCA and pre-classifier. The prediction accuracy is about 86.5%.
实验结果表明，使用组合KFDA的方法预测的效果优于FDA和PCA以及单个KFDA分类器的预测效果，预测准确率为86.5%。
The results indicate that the performance of ensembles of KFDA is better than that of FDA, PCA and pre-classifier. The prediction accuracy is about 86.5%.
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