计算机工程 ›› 2012, Vol. 38 ›› Issue (9): 180-182.doi: 10.3969/j.issn.1000-3428.2012.09.054

• 人工智能及识别技术 • 上一篇    下一篇

基于信任权学习的在线分类算法

刘建伟,池光辉,罗雄麟   

  1. (中国石油大学(北京)自动化研究所,北京 102249)
  • 收稿日期:2011-06-07 出版日期:2012-05-05 发布日期:2012-05-05
  • 作者简介:刘建伟(1966-),男,副研究员、博士,主研方向:机器学习;池光辉,硕士研究生;罗雄麟,教授、博士

Online Classification Algorithm Based on Confidence-weighted Learning

LIU Jian-wei, CHI Guang-hui, LUO Xiong-lin   

  1. (Research Institute of Automation, China University of Petroleum, Beijing 102249, China)
  • Received:2011-06-07 Online:2012-05-05 Published:2012-05-05

摘要: 鉴于高斯过程对处理高维数、小样本、非线性等复杂问题具有较好的适应性,将其引入到在线分类器学习算法中,形成一种新型的在线分类算法,即信任权算法。该算法的信任权超参数为模型向量的高斯分布,每训练一次样本就修正一次模型向量的信任权,并使样本正确分类的概率在某个特定信任域内。采用人工和实际数据进行实验,结果表明信任权算法优于传统的感知器算法。

关键词: 信任权学习, 感知器, 在线学习, 大间隔, 高斯分布

Abstract: Gaussian process has the ability to deal with complicated problems such as high dimension, small samples, and nonlinearity. A new algorithm called confidence-weighted learning is proposed by introducing Gaussian process into the online classification. It maintains a Gaussian distribution over weight vectors and makes adjustment over the weight vector with every sample instance. By this way, it correctly classifies examples with a specific probability. Experimental results show that the confidence-weighted classifiers are always more accurate than the perceptron classifier in the simulated datasets, as well as in the real datasets.

Key words: confidence-weighted learning, perceptron, online learning, large margin, Gaussian distribution

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