2.765

2022影响因子

(CJCR)

  • 中文核心
  • EI
  • 中国科技核心
  • Scopus
  • CSCD
  • 英国科学文摘

留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

基于指数损失和0-1损失的在线Boosting算法

侯杰 茅耀斌 孙金生

侯杰, 茅耀斌, 孙金生. 基于指数损失和0-1损失的在线Boosting算法. 自动化学报, 2014, 40(4): 635-642. doi: 10.3724/SP.J.1004.2014.00635
引用本文: 侯杰, 茅耀斌, 孙金生. 基于指数损失和0-1损失的在线Boosting算法. 自动化学报, 2014, 40(4): 635-642. doi: 10.3724/SP.J.1004.2014.00635
HOU Jie, MAO Yao-Bin, SUN Jin-Sheng. Online Boosting Algorithms Based on Exponential and 0-1 Loss. ACTA AUTOMATICA SINICA, 2014, 40(4): 635-642. doi: 10.3724/SP.J.1004.2014.00635
Citation: HOU Jie, MAO Yao-Bin, SUN Jin-Sheng. Online Boosting Algorithms Based on Exponential and 0-1 Loss. ACTA AUTOMATICA SINICA, 2014, 40(4): 635-642. doi: 10.3724/SP.J.1004.2014.00635

基于指数损失和0-1损失的在线Boosting算法

doi: 10.3724/SP.J.1004.2014.00635
基金项目: 

国家自然科学基金(60974129)资助

详细信息
    作者简介:

    侯杰 南京理工大学自动化学院博士研究生.主要研究方向为视觉目标检测与跟踪,模式识别与机器学习.E-mail:reiase@gmail.com

Online Boosting Algorithms Based on Exponential and 0-1 Loss

Funds: 

Supported by National Natural Science Foundation of China (60974129)

  • 摘要: 推导了使用指数损失函数和0-1损失函数的Boosting 算法的严格在线形式,证明这两种在线Boosting算法最大化样本间隔期望、最小化样本间隔方差.通过增量估计样本间隔的期望和方差,Boosting算法可应用于在线学习问题而不损失分类准确性. UCI数据集上的实验表明,指数损失在线Boosting算法的分类准确性与批量自适应 Boosting (AdaBoost)算法接近,远优于传统的在线Boosting;0-1损失在线Boosting算法分别最小化正负样本误差,适用于不平衡数据问题,并且在噪声数据上分类性能更为稳定.
  • [1] Freund Y, Schapire R E, Abe N. A short introduction to Boosting. Journal-Japanese Society for Artificial Intelligence, 1999, 14(5): 771-780
    [2] Freund Y, Schapire R E. A desicion-theoretic generalization of on-line learning and an application to Boosting. Journal of Computer and System Sciences, 1997, 55(1): 119-139
    [3] Cao Ying, Miao Qi-Guang, Liu Jia-Chen, Gao Lin. Advance and prospects of Adaboost algorithm. Acta Automatica Sinica, 2013, 39(6): 745-758(曹莹, 苗启广, 刘家辰, 高琳. Adaboost算法研究进展与展望. 自动化学报, 2013, 39(6): 745-758)
    [4] Viola P, Jones M J. Robust real-time face detection. International Journal of Computer Vision, 2004, 57(2): 137-154
    [5] Zhang C, Zhang Z Y. A Survey of Recent Advances in Face Detection, Technical Report MSR-TR-2010-66, Microsoft Research, Redmond, WA, 2010
    [6] Wu J X, Rehg J M, Mullin M D. Learning a rare event detection cascade by direct feature selection. [Online], available: http: //papers.nips.cc/paper/2353-learning-a-rare-event-detection-cascade-by-direct-feature-selection.pdf, October 25, 2012
    [7] Bartlett P, Freund Y, Lee W S, Schapire R E. Boosting the margin: a new explanation for the effectiveness of voting methods. The Annals of Statistics, 1998, 26(5): 1651-1686
    [8] Grabner H, Grabner M, Bischof H. Real-time tracking via on-line Boosting. In: Proceedings of the 2006 British Machine Vision Conference. Edinburgh, British, 2006, 1: 4756
    [9] Kuo C H, Nevatia R. How does person identity recognition help multi-person tracking? In: Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Providence, RI: IEEE, 2011. 1217-1224
    [10] Yang B, Nevatia R. Multi-target tracking by online learning of non-linear motion patterns and robust appearance models. In: Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Providence, RI: IEEE, 2012. 1918-1925
    [11] Grabner H, Bischof H. On-line Boosting and vision. In: Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. New York, NY: IEEE, 2006, 1: 260-267
    [12] Liu X M, Yu T. Gradient feature selection for online Boosting. In: Proceedings of the 11th IEEE International Conference on Computer Vision (ICCV). Rio de Janeiro: IEEE, 2007. 1-8
    [13] Oza N C. Online Ensemble Learning [Ph.D. dissertation], The University of California, Berkeley, 2001
    [14] Grabner H, Leistner C, Bischof H. Semi-supervised on-line Boosting for robust tracking. Computer Vision-ECCV 2008. Berlin Heidelberg: Springer, 2008: 234-247
    [15] Babenko B, Yang M H, Belongie S. Robust object tracking with online multiple instance learning. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2011, 33(8): 1619-1632
    [16] Shen C H, Li H X. On the dual formulation of Boosting algorithms. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2010, 32(12): 2216-2231
    [17] Friedman J H. Greedy function approximation: a gradient Boosting machine. Annals of Statistics, 2001, 29(5): 11891232
    [18] Vapnik V. The Nature of Statistical Learning Theory. New York: Springer, 2000
    [19] Wu J, Brubaker S C, Mullin M D, Rehg J M. Fast asymmetric learning for cascade face detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2008, 30(3): 369-382
    [20] Ge Jun-Feng, Luo Yu-Pin. A comprehensive study for asymmetric Adaboost and its application in object detection. Acta Automatica Sinica, 2009, 35(11): 1403-1409 (葛俊锋, 罗予频. 非对称AdaBoost算法及其在目标检测中的应用. 自动化学报, 2009, 35(11): 1403-1409)
    [21] He H B, Garcia E A. Learning from imbalanced data. Transactions on Knowledge and Data Engineering, 2009, 21(9): 1263-1284
    [22] Li Qiu-Jie, Mao Yao-Bin, Wang Zhi-Quan. Research on Boosting-based imbalanced data classification. Computer Science, 2011, 38(12): 224-228 (李秋洁, 茅耀斌, 王执铨. 基于Boosting的不平衡数据分类算法研究. 计算机科学, 2011, 38(12): 224-228)
    [23] Li Qiu-Jie, Mao Yao-Bin. AUC optimization Boosting based on data rebalance. Acta Automatica Sinica, 2013, 39(9): 1467-1475 (李秋洁, 茅耀斌. 基于数据重平衡的AUC 优化Boosting算法. 自动化学报, 2013, 39(9): 1467-1475)
    [24] Sun Y M, Kamel M S, Wong A K C, Wang Y. Cost-sensitive Boosting for classification of imbalanced data. Pattern Recognition, 2007, 40(12): 3358-3378
  • 加载中
计量
  • 文章访问数:  2492
  • HTML全文浏览量:  128
  • PDF下载量:  1384
  • 被引次数: 0
出版历程
  • 收稿日期:  2013-06-05
  • 修回日期:  2013-10-25
  • 刊出日期:  2014-04-20

目录

    /

    返回文章
    返回