The multiple classifiers combination fuses the decision level data.
多分类器组合是对决策层的数据进行融合。
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。
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。
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