For the project:
  • Details on the implemented Classifiers
Further reading:
  • Semi-Supervised Learning
    • Te Ming Huang, Vojislav Kecman: Performance Comparisons of Semi-Supervised Learning Algorithms. In: ICML 05. [www]

    • Olivier Chapelle, Alexander Zien: Semi-Supervised Classification by Low Density Separation. Proceedings of the Tenth International Workshop on Artificial Intelligence and Statistics, 57-64 (2005). [www]

    • Dengyong Zhou, Olivier Bousquet, Thomas Navin Lal, Jason Weston and Bernhard Schoelkopf: Learning with Local and Global Consistency. In: NIPS 2003, Vancouver, Canada (2004). [www]

    • Huang, T.M., Kecman, V.: Semi-supervised Learning from Unbalanced Labeled Data ? An Improvement. In 'Knowledge Based and Emergent Technologies Relied Intelligent Information and Engineering Systems', Eds. Negoita, M. Gh., at al., Lecture Notes on Computer Science 3215, pp. 765-771, Springer Verlag, Heidelberg, 2004. [www]

    • A. Demirez and K. Bennett: Optimization approaches to Semisupervised Learning. Applications and Algorithms of Complementarity. Kluwer Academic Publishers, Boston, 2000. [www]

    • K. Bennet and A. Demiriz: Semi-supervised support vector machines. In Advances in Neural Information Processing Systems 11, pages 368-374. MIT Press, 1998. [www]

    • M. Seeger: Learning with labeled and unlabeled data. Technical report, Institute for Adaptive and Neural Computation, University of Edinburgh, 2001. [www]

    • M. Seeger: An Overview of Semi-Supervised Learning. Invited Talk ICML 05, Workshop on SSL, 2005. [www]

  • Transductive Learning
    • T. Joachims: Transductive Inference for Text Classification using Support Vector Machines. Proceedings of the International Conference on Machine Learning (ICML), 1999. [www]

  • Other
    • Ting, K. M. and Witten, I. H.: Stacking Bagged and Dagged Models. In Proceedings of the Fourteenth international Conference on Machine Learning (July 08 - 12, 1997). D. H. Fisher, Ed. Morgan Kaufmann Publishers, San Francisco, CA, 367-375. [www]