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。
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。
Compared with the single suppo vector machine method, the support vector machine ensemble method has better classification accuracy.
模拟实验结果表明，该方法具有明显优于单一支持向量机的更高的分类准确率。
The paper proposes a medical image classification based on ensemble leaning.
提出了一个基于集成学习方法的医学图像分类器。
It can also be used to generate an ensemble of classification systems to suit different risk preferences in investment.
此外，它们还可以形成一个新的分类学科来应对不同的风险投资。
Selective ensemble classifiers can improve classification accuracy rate of data set. But for a specific data classification, the classifiers contained by ensemble can not be the best combination.
选择性集成分类算法虽能提高集合分类器在整体数据集上的分类性能，但针对某一具体数据进行分类时，其选择出的个体分类器集合并不一定是最优组合。
Multiple classifiers ensemble is an effective method to solve complex classification problems in pattern recognition field.
多分类器组合是解决复杂模式识别问题的有效办法。
Multiple classifiers ensemble is an effective method to solve complex classification problems in pattern recognition field.
多分类器组合是解决复杂模式识别问题的有效办法。
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