一个好的集成学习算法，关键是能生成差异度大的个体分类器。
The key of a good ensemble learning algorithm is able to generate the diversity of individual learners.
由于Q统计量在实验中效果不错，因此本文采用Q统计量度量两个分类器之间的差异度。提出一种采用Q统计量的选择性集成学习算法。
As the Q statistic is better in experiments, so this paper uses Q statistic to measure the diversity between the two learners and proposes a new selective ensemble algorithm basing Q statistic.
集成学习是当前机器学习的一个研究热点，它可以提高分类算法的泛化性能。
Ensemble learning is a research hotspot in machine learning, which can improve generalization performance of classification algorithm.
因此选择性集成已成为集成学习的一个重要研究方向，其更好的选择策略以及算法运算速度的提高有待更多研究人员的深入研究。
So it has become an important research topic of ensemble learning. A better selection strategy and improvement of the speed of algorithm need more researches.
因此选择性集成已成为集成学习的一个重要研究方向，其更好的选择策略以及算法运算速度的提高有待更多研究人员的深入研究。
So it has become an important research topic of ensemble learning. A better selection strategy and improvement of the speed of algorithm need more researches.
应用推荐