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.
应用推荐