This paper studies the design of pattern recognition system based multiple classifiers combination.
本文对多分类器融合模式识别的设计方法进行了研究。
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。
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。
Based on thought of multiple classifiers combination method, this paper proposes a combination classification method of multiple decision trees based on PSO Algorithm.
针对数据挖掘中的分类问题，依据组合分类方法的思想，提出一种基于遗传算法的多重决策树组合分类方法。
The combination of multiple classifiers is one of the effective ways to improve the recognition performance.
多分类器组合是提高识别效果的一条有效途径。
A new method of speech emotion recognition via voting combination of multiple classifiers is proposed for improving speech emotion classification rate.
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.
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。
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