A Constructive Learning Algorithm for Text Categorization

  • Weijun Chen
  • Bo Zhang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3972)


The paper presents a new constructive learning algorithm CWSN (Covering With Sphere Neighborhoods) for three-layer neural networks, and uses it to solve the text categorization (TC) problem. The algorithm is based on a geometrical representation of M-P neuron, i.e., for each category, CWSN tries to find a set of sphere neighborhoods which cover as many positive documents as possible, and don’t cover any negative documents. Each sphere neighborhood represents a covering area in the vector space and it also corresponds to a hidden neuron in the network. The experimental results show that CWSN demonstrates promising performance compared to other commonly used TC classifiers.


Input Vector Hide Neuron Text Categorization Neural Classifier Sphere Neighborhood 
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  2. Yang, Y., Xin, L.: A Re-examination of Text Categorization Methods. In: 22th International Conference on Research and Development in Information Retrieval, SIGIR 1999, pp. 42–49 (1999)Google Scholar
  3. Zhang, L., Zhang, B.: A Geometrical Representation of McCulloch-Pitts Neural Model and Its Applications. IEEE Transactions on Neural Networks 10(4), 925–929 (1999)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Weijun Chen
    • 1
  • Bo Zhang
    • 2
  1. 1.School of SoftwareTsinghua UniversityBeijingP.R. China
  2. 2.Department of Computer ScienceTsinghua UniversityBeijingP.R. China

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