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A Simple and Effective Lagrangian-Based Combinatorial Algorithm for S\(^3\)VMs

  • Francesco BagattiniEmail author
  • Paola Cappanera
  • Fabio Schoen
Conference paper
First Online:
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10710)

Abstract

Many optimization techniques have been developed in the last decade to include the unlabeled patterns in the Support Vector Machines formulation. Two broad strategies are followed: continuous and combinatorial. The approach presented in this paper belongs to the latter family and is especially suitable when a fair estimation of the proportion of positive and negative samples is available. Our method is very simple and requires a very light parameter selection. Experiments on both artificial and real-world datasets have been carried out, proving the effectiveness and the efficiency of the proposed algorithm.

Keywords

Semi-supervised learning Support vector machines Lagrangian combinatorial heuristics 
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References

  1. 1.
    Bertsekas, D.P.: Nonlinear Programming. Athena Scientific (2003)Google Scholar
  2. 2.
    Chang, C.C., Lin, C.J.: LIBSVM: a library for support vector machines. ACM Trans. Intell. Syst. Technol. 2, 27:1–27:27 (2011)CrossRefGoogle Scholar
  3. 3.
    Chapelle, O., Chi, M., Zien, A.: A continuation method for semi-supervised SVMs. In: ICML 2006, pp. 185–192. Max-Planck-Gesellschaft, ACM Press, New York, June 2006Google Scholar
  4. 4.
    Chapelle, O., Sindhwani, V., Keerthi, S.S.: Branch and bound for semi-supervised support vector machines. Technical report, Max Plank Institute (2006)Google Scholar
  5. 5.
    Chapelle, O., Sindhwani, V., Keerthi, S.S.: Optimization techniques for semi-supervised support vector machines. J. Mach. Learn. Res. 9, 203–233 (2008)zbMATHGoogle Scholar
  6. 6.
    Collobert, R., Sinz, F., Weston, J., Bottou, L.: Large scale transductive SVMs. J. Mach. Learn. Res. 7, 1687–1712 (2006)MathSciNetzbMATHGoogle Scholar
  7. 7.
    De Bie, T., Cristianini, N.: Semi-supervised Learning Using Semi-definite Programming. MIT Press, Cambridge (2006)Google Scholar
  8. 8.
    Gieseke, F., Airola, A., Pahikkala, T., Kramer, O.: Fast and simple gradient-based optimization for semi-supervised support vector machines. Neurocomputing 123, 23–32 (2014). Contains Special issue articles: Advances in Pattern Recognition Applications and MethodsCrossRefGoogle Scholar
  9. 9.
    Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning. Springer Series in Statistics. Springer, New York (2001).  https://doi.org/10.1007/978-0-387-84858-7CrossRefzbMATHGoogle Scholar
  10. 10.
    Joachims, T.: Transductive inference for text classification using support vector machines. In: Proceedings of the Sixteenth International Conference on Machine Learning, ICML 1999, pp. 200–209. Morgan Kaufmann Publishers Inc., San Francisco (1999)Google Scholar
  11. 11.
    Li, B., Yang, Z., Zhu, Y., Meng, H., Levow, G., King, I.: Predicting user evaluations of spoken dialog systems using semi-supervised learning. In: 2010 IEEE Spoken Language Technology Workshop (SLT) (2010)Google Scholar
  12. 12.
    Li, Y.F., Zhou, Z.H.: Towards making unlabeled data never hurt. IEEE Trans. Pattern Anal. Mach. Intell. 37(1), 175–188 (2015)CrossRefGoogle Scholar
  13. 13.
    Li, Y., Tsang, I.W., Kwok, J.T., Zhou, Z.: Convex and scalable weakly labeled SVMs. CoRR abs/1303.1271 (2013)Google Scholar
  14. 14.
    Nene, S.A., Nayar, S.K., Murase, H.: Columbia Object Image Library (COIL-100). Technical report, February 1996Google Scholar
  15. 15.
    Sindhwani, V., Keerthi, S.S., Chapelle, O.: Deterministic annealing for semi-supervised kernel machines. In: Proceedings of the 23rd International Conference on Machine Learning, ICML 2006, pp. 841–848. ACM, New York (2006)Google Scholar
  16. 16.
    Timsina, P., Liu, J., El-Gayar, O., Shang, Y.: Using semi-supervised learning for the creation of medical systematic review: an exploratory analysis. In: 2016 49th Hawaii International Conference on System Sciences (HICSS) (2016)Google Scholar
  17. 17.
    Vapnik, V.N., Sterin, A.: On structural risk minimization or overall risk in a problem of pattern recognition. Autom. Remote Control 10(3), 1495–1503 (1977)zbMATHGoogle Scholar
  18. 18.
    Yang, W., Yin, X., Xia, G.S.: Learning high-level features for satellite image classification with limited labeled samples. IEEE Trans. Geosci. Remote Sens. 53(8), 4472–4482 (2015)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Francesco Bagattini
    • 1
    Email author
  • Paola Cappanera
    • 1
  • Fabio Schoen
    • 1
  1. 1.DINFOUniversità degli Studi di FirenzeFirenzeItaly

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