The Nanjing University Professor Zhi-Hua Zhou proposed the concept of selective ensemble learning.
南京大学周志华教授提出选择性集成学习的概念。
The diversity in the ensemble learning is an important concept.
差异度是集成学习中的一个重要概念。
Diversity among base classifiers is known to be an important factor for improving generalization performance in ensemble learning.
差异性是提高分类器集成泛化性能的重要因素。
The key of a good ensemble learning algorithm is able to generate the diversity of individual learners.
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。
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。
Ensemble learning is a general method for classifying data streams.
对数据流分类分析的常用方法是集成学习。
The Ensemble Learning is a hotspot in the intelligent learning field.
在智能学习领域，集成学习是一个新的研究热点。
Ensemble learning is a research hotspot in machine learning, which can improve generalization performance of classification algorithm.
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.
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。
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