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。
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。
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 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。
It includes the origin, definition, the main technical, and diversity of the selective ensemble learning.
包括选择性集成学习的起源、定义、主要技术，和集成学习中流行的差异度。
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。
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。
The application of ensemble learning in face recognition is gradually extending and the inherently distinctive of the system improves distinctly.
因此，很多人开始将集成学习和人脸识别联系起来。
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。
Stepping up the meadow slope the ensemble is further embedded into the village-scape while remaining legible as a place of learning, research, and exchange.
走在牧场草坡上，整个融入到了乡村景观之中，但仍保留清晰的学习、研究和交流的空间。
The overall ensemble (redevelopment and extension) is convincing down to the details and offers the children worlds for learning and movement.
整个工程（重建和扩展）的细节令人信服，它为孩子们提供了学习和运动的场所。
To resolve combining classifiers decisions among ensemble classification over data streams without labeled examples, a transductive constraint-based learning strategy was proposed.
为了解决在没有已知标签样本的情况下数据流组合分类决策问题，提出一种基于约束学习的数据流组合分类器的融合策略。
For increasing ensemble diversity to improve the generalization ability of learning system, three methods is given in order to create diverse neural networks.
文中还给出了三种生成差异度不同的神经元网络的方法，并通过求解实例对比了这三种方法的优化效果。
For increasing ensemble diversity to improve the generalization ability of learning system, three methods is given in order to create diverse neural networks.
文中还给出了三种生成差异度不同的神经元网络的方法，并通过求解实例对比了这三种方法的优化效果。
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