• ISSN 0258-2724
  • CN 51-1277/U
  • EI Compendex
  • Scopus
  • Indexed by Core Journals of China, Chinese S&T Journal Citation Reports
  • Chinese S&T Journal Citation Reports
  • Chinese Science Citation Database
Volume 19 Issue 3
Jun.  2006
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Article Contents
XU Min, ZHANG Liping, ZHU Wujia. Document Classification by Semi-supervised Online Learning Based on ART[J]. Journal of Southwest Jiaotong University, 2006, 19(3): 335-340.
Citation: XU Min, ZHANG Liping, ZHU Wujia. Document Classification by Semi-supervised Online Learning Based on ART[J]. Journal of Southwest Jiaotong University, 2006, 19(3): 335-340.

Document Classification by Semi-supervised Online Learning Based on ART

  • Received Date: 05 Sep 2005
  • Publish Date: 25 Jun 2006
  • A semi-supervised learning system was proposed based on ART(adaptive resonance theory).It overcomes the limitation in the assumption in other semi-supervised learning algorithms that probabilistic distribution of data is known,and has the strong ability of learning new patterns and correcting errors because of stability and plasticity of the adaptive resonance theory.Higher adaptability of the system was advanced by setting vigilance parameters dynamically.Experimental results illustrate that the performances of the proposed system is better than the discriminant CEM(classification expectation maximization) algorithm,particularly when there are noise data and new patterns.

     

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  • ZHANG T,OLES F.A probability analysis on the value of unlabeled data for classification problems[C] ∥Proc.Intl Conf.Machine Learning (ICML).San Francisco:Morgan Kaufmann,2000:1 191-1 198.[2] COHEN I,COZMAN F G,BRONSTEIN A.On the value of unlabeled data in semi-supervised learning based on maximum-likelihood estimation[R]. Technical Report HPL-2002-140,Hewlett-Packard Labs,2002.[3] CARPENTER G A,GROSSBERG S,REYNOLDS J H.ARTMAP:supervised real-time learning and classification of non-stationary data by a self-organizing neural network[J]. Neural Networks,1991,4:565-588.[4] CARPENTER G A,GROSSBERG S.A massively parallel architecture for a self-organizing neural pattern recognition machine[J]. Computer Vision,Graphics,and Image Processing,1987,37:54-115.[5] KOLLER D,SAHAMI M.Hierarchically classifying documents using very few words[C] ∥Proc.ICML-97,Nashville:Morgan Kaufmann,1997:170-176.[6] NOEL V J,REZA A M,PATRICK Gallinari.Learning classification with both labeled and unlabeled data[C] ∥Proc.European Conference on Machine Learning,Lecture Notes in AI.Helsinki:Springer,2002:468-479.
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