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使用聚类来加速AdaBoost并实现噪声数据探测 被引量：2

Using Clustering to Speed up AdaBoost and Detecting Noisy Data
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摘要 决定集成学习性能的主要因素是集成的个体学习器之间的差异性.使用聚类技术来加速AdaBoost.在不同噪声水平环境下,新算法的性能都接近AdaBoost.对AdaBoost噪声敏感问题提出了新的解决思路,使用该项技术可以实现快速的噪声探测和噪声剔除后的再学习,从而在对含噪声数据基进行处理时,在综合性能和效率上都明显优于AdaBoost. According as the main factor deciding the performance of ensemble learning is the diversity of component learners, clustering technology is used to speed up AdaBoost in this paper. The performance of the new algorithm is very close to the AdaBoost in learning deferent noise levels data sets. The new algorithm can be used to detect and eliminate noisy data quickly, and could achieve rapid learning on data sets after eliminating noise. It overcomes the noise-sensitive shortcoming of AdaBoost. The general performance and efficiency of the new algorithm are much better than AdaBoost in processing data sets containing noise.
作者 谢元澄 杨静宇
机构地区 南京农业大学信息科学技术学院 南京理工大学计算机科学与技术学院
出处 《软件学报》 EI CSCD 北大核心 2010年第8期1889-1897,共9页 Journal of Software
基金 国家自然科学基金No.60632050~~
关键词 ADABOOST 聚类 个体学习器 BP神经网络 加速 噪声检测 AdaBoost clustering component learner BP neural network speed up noise detection
分类号 TP181 [自动化与计算机技术—控制理论与控制工程]
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参考文献17

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同被引文献16

  • 1武勃,黄畅,艾海舟,劳世竑.基于连续Adaboost算法的多视角人脸检测[J].计算机研究与发展,2005,42(9):1612-1621. 被引量：66
  • 2姜远,周志华.基于词频分类器集成的文本分类方法[J].计算机研究与发展,2006,43(10):1681-1687. 被引量：22
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软件学报
2010年 第8期

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