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计算机工程 ›› 2012, Vol. 38 ›› Issue (7): 125-127.doi: 10.3969/j.issn.1000-3428.2012.07.041

• 人工智能及识别技术 • 上一篇    下一篇

新的软间隔AdaBoost弱分类器权重调整算法

董银丽1，周水生2，高 艳2   

  1. (1. 西安欧亚学院基础部，西安 710065；2. 西安电子科技大学理学院，西安 710071)
  • 收稿日期:2011-07-08 出版日期:2012-04-05 发布日期:2012-04-05
  • 作者简介:董银丽(1971－)，女，讲师、硕士，主研方向：最优化理论及算法；周水生，副教授、博士；高 艳，硕士
  • 基金项目:
    国家自然科学基金资助项目(61179040)；中央高校基本科研业务费专项基金资助项目(JY10000970007)

New Weights Adjusting Algorithm of Soft Margin Weak Classifiers for AdaBoost

DONG Yin-li 1, ZHOU Shui-sheng 2, GAO Yan 2   

  1. (1. Foundation Department, Xi’an Eurasia University, Xi’an 710065, China; 2. School of Science, Xidian University, Xi’an 710071, China)
  • Received:2011-07-08 Online:2012-04-05 Published:2012-04-05

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1035

被引次数

1

摘要： 为避免硬间隔算法过分强调较难分类样本而导致泛化性能下降的问题，提出一种新的基于软间隔的AdaBoost-QP算法。在样本硬间隔中加入松弛项，得到软间隔的概念，以优化样本间隔分布、调整弱分类器的权重。实验结果表明，该算法能降低泛化误差，提高 AdaBoost算法的泛化性能。

关键词: 机器学习, 弱分类器, AdaBoost算法, 软间隔, 泛化性能

Abstract: In order to overcome the drawback that a low generalization performance is reached since the bone samples are emphasized too much in hard margin algorithms, this paper presents a new weights adjusting algorithm of soft margin weak classifiers for AdaBoost. Based on adjusting the combination coefficients of weak classifiers, a soft margin is defined by adding a slack item to the hard margin, and a new soft margin AdaBoost-QP algorithm is proposed to optimize the margin distribution of the samples to adjust the weights of weak classifiers by a Quadratic Programming(QP). Experimental results show that the new algorithm can decrease generalization error, and improve the performance of AdaBoost.

Key words: machine learning, weak classifier, AdaBoost algorithm, soft margin, generalization performance

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