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。
Combining Multiple Classifiers can be viewed as a novel hybrid system to achieve high recognition accuracy for Text Independent Speaker Identification.
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。
The experimental results have shown that Combining Multiple Classifiers with different features can result in satisfactory and significant improvement in recognition performance.
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 method of combining multiple classifiers developed in this paper has the same performance in classification of remote sensing images which have different landscape characteristics.
组合方法在不同景观特征地区的遥感分类中具有相同的性能特征。
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.
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。
The voting algorithms try to form a powerful classifier by combining multiple weak classifiers as a council of base-classifiers.
基于委员会的方法试图通过合并多个弱分类器建立一个有效的委员会来构造一个更加有效的分类器。
The voting algorithms try to form a powerful classifier by combining multiple weak classifiers as a council of base-classifiers.
基于委员会的方法试图通过合并多个弱分类器建立一个有效的委员会来构造一个更加有效的分类器。
应用推荐