A Novel T2-SVM for Partially Supervised Classification

  • Lorenzo Bruzzone
  • Mattia Marconcini
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3776)

Abstract

This paper addresses partially supervised classification problems, i.e. problems in which different data sets referring to the same scenario (phenomenon) should be classified but a training information is available only for some of them. In particular, we propose a novel approach to the partially supervised classification which is based on a Bi-transductive Support Vector Machines (T2-SVM). Inspired by recently proposed Transductive SVM (TSVM) and Progressive Transductive SVM (PTSVM) algorithms, the T2-SVM algorithm extracts information from unlabeled samples exploiting the transductive inference, thus obtaining high classification accuracies. After defining the formulation of the proposed T2-SVM technique, we also present a novel accuracy assessment strategy for the validation of the classification performances. The experimental results carried out on a real remote sensing partially supervised problem confirmed the reliability and the effectiveness of both the T2-SVM and the corresponding validation procedure.

Keywords

Support Vector Machine Classification Accuracy Kappa Coefficient Training Pattern High Classification Accuracy 
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.

References

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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Lorenzo Bruzzone
    • 1
  • Mattia Marconcini
    • 1
  1. 1.Dept. of Information and Communication TechnologyUniversity of TrentoPovo, TrentoItaly

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