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The classifier fusion approaches include Maximum, Minimum, Product, Mean, Median, Major Voting fusion methods and decision template fusion methods.
对分类器融合采用极大值法、极小值法、乘积法、均值法、中值法、投票法和各种决策模板融合方法。
Simulation result showed that target recognition ratio can be improved by 7.8% with the modified algorithm, which shows that the decision template method is effective.
仿真结果表明改进的决策模板法比经典的决策模板法识别率提高7.8% ，是一种比较有效的识别方法。
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。
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