Abstract
We introduce and describe the Hybrid Semi-Supervised Method (HSSM) for learning. This is the first hybrid method aimed to solve problems with both labeled and unlabeled data. The new method uses an unsupervised stage in order to decompose the full problem into a set of simpler subproblems. HSSM applies simple stopping criteria during the unsupervised stage, which allows the method to concentrate on the difficult portions of the original problem. The new algorithm also makes use of a simple strategy to select at each subproblem a small subset of unlabeled samples that are relevant to modify the decision surface. To this end, HSSM trains a linear SVM on the available labeled samples, and selects the unlabeled samples that lie within the margin of the trained SVM. We evaluated the new method using a previously introduced setup, which includes datasets with very different properties. Overall, the error levels produced by the new HSSM are similar to other SSL methods, but HSSM is shown to be more efficient than all previous methods, using only a small fraction of the available unlabeled data.
Chapter PDF
Similar content being viewed by others
References
Ahumada, H.C., Grinblat, G.L., Granitto, P.M.: Unsupervized Data-Driven Partitioning of Multiclass Problems. In: Honkela, T. (ed.) ICANN 2011, Part I. LNCS, vol. 6791, pp. 117–125. Springer, Heidelberg (2011)
Blum, A., Chawla, S.: Learning from labeled and unlabeled data using graph mincuts. In: ICML 18, pp. 19–26. Morgan Kaufmann, San Francisco (2001)
Chapelle, O., Schölkopf, B., Zien, A. (eds.): Semi-Supervised Learning. MIT Press, Cambridge (2006)
Chapelle, O., Sindhwani, V., Keerthi, S.: Branch and bound for semi-supervised support vector machines. In: NIPS 19. MIT Press, Cambridge (2007)
Chapelle, O., Zien, A.: Semi-supervised classification by low density separation. In: AISTATS 2005, pp. 57–64 (2005)
Cristianini, N., Shawe–Taylor, J.: An Introduction to Support Vector Machines. Cambridge University Press, Cambridge (2000)
Delalleau, O., Bengio, Y., Le Roux, N.: Large-scale algorithms. In: Chapelle, O., Schölkopf, B., Zien, A. (eds.) Semi-Supervised Learning, pp. 333–341. MIT Press, Cambridge (2006)
Grandvalet, Y., Bengio, Y.: Semi-supervised learning by entropy minimization. In: Actes de CAP 2005, pp. 281–296 (2005)
Joachims, T.: Transductive inference for text classification using support vector machines. In: ICML 16, pp. 200–209. Morgan Kaufmann Publishers, San Francisco (1999)
Lawrence, N.D., Jordan, M.I.: Semi-supervised learning via gaussian processes. In: NIPS 17, pp. 753–760. MIT Press, Cambridge (2004)
Li, Y.-F., Zhou, Z.-H.: Improving semi-supervised support vector machines through unlabeled instances selection. In: Burgard, W., Roth, D. (eds.) AAAI. AAAI Press (2011)
Singh, A., Nowak, R.D., Zhu, X.: Unlabeled data: Now it helps, now it doesn’t. In: NIPS 21, pp. 1513–1520 (2008)
Sneath, P.H.A., Sokal, R.R.: Numerical Taxonomy. W.H. Freeman and Company, San Francisco (1973)
Zhu, X., Goldberg, A.B.: Introduction to Semi-Supervised Learning. Morgan & Claypool Publishers, California (2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ahumada, H.C., Granitto, P.M. (2012). A Simple Hybrid Method for Semi-Supervised Learning. In: Alvarez, L., Mejail, M., Gomez, L., Jacobo, J. (eds) Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. CIARP 2012. Lecture Notes in Computer Science, vol 7441. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33275-3_17
Download citation
DOI: https://doi.org/10.1007/978-3-642-33275-3_17
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-33274-6
Online ISBN: 978-3-642-33275-3
eBook Packages: Computer ScienceComputer Science (R0)