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A Multiclass Classification Framework for Document Categorization

  • Qi Qiang
  • Qinming He
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3872)

Abstract

With a great amount of textual information are available on the Internet and corporate intranets, it has become a necessary to categorize large documents. As we known, text classification problem is representative multiclass problem. This paper describes a framework, which we call Strong-to-Weak- to-Strong (SWS). It transforms a “strong” learning algorithm to a “weak” algorithm by decreasing its iterative numbers of optimization while preserving its other characteristics like geometric properties and then makes use of the kernel trick for “weak” algorithms to work in high dimensional spaces, finally improves the performances of text classification. We analyzed the particular properties of learning with text and identified why this approach is appropriate for this task. Empirical results show that our approach is competitive with the other methods.

Keywords

Mahalanobis Distance Binary Classifier Document Categorization Output Code Kernel Trick 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Qi Qiang
    • 1
  • Qinming He
    • 1
    • 2
  1. 1.College of Computer ScienceZhejiang UniversityHangzhouChina
  2. 2.Ningbo Institute of TechnologyZhejiang UniversityNingboChina

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