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。
We investigate the theoretical framework of multiple classifiers fusion, and apply the decision template algorithms to classify the protein secondary structural classes.
分析了多分类器融合算法的理论框架，并采用决策模板算法对蛋白质结构类的预测问题进行了研究。
According to the continuous space model for emotion, an improved queuing voting algorithm was proposed to implement the fusion of multiple emotion classifiers for a good emotion recognition result.
分别利用普通话情感语音库和德语情感语音库进行实验，结果表明，与几种传统融合算法相比，改进的排序式选举法能够取得更好的融合效果，其识别性能明显优于单分类器。
According to the continuous space model for emotion, an improved queuing voting algorithm was proposed to implement the fusion of multiple emotion classifiers for a good emotion recognition result.
分别利用普通话情感语音库和德语情感语音库进行实验，结果表明，与几种传统融合算法相比，改进的排序式选举法能够取得更好的融合效果，其识别性能明显优于单分类器。
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