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.
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The idea of employing Lagrangian techniques to relax the balance constraint is proposed also in [15], but for a totally different formulation.
References
Bertsekas, D.P.: Nonlinear Programming. Athena Scientific (2003)
Chang, C.C., Lin, C.J.: LIBSVM: a library for support vector machines. ACM Trans. Intell. Syst. Technol. 2, 27:1–27:27 (2011)
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 2006
Chapelle, O., Sindhwani, V., Keerthi, S.S.: Branch and bound for semi-supervised support vector machines. Technical report, Max Plank Institute (2006)
Chapelle, O., Sindhwani, V., Keerthi, S.S.: Optimization techniques for semi-supervised support vector machines. J. Mach. Learn. Res. 9, 203–233 (2008)
Collobert, R., Sinz, F., Weston, J., Bottou, L.: Large scale transductive SVMs. J. Mach. Learn. Res. 7, 1687–1712 (2006)
De Bie, T., Cristianini, N.: Semi-supervised Learning Using Semi-definite Programming. MIT Press, Cambridge (2006)
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 Methods
Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning. Springer Series in Statistics. Springer, New York (2001). https://doi.org/10.1007/978-0-387-84858-7
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)
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)
Li, Y.F., Zhou, Z.H.: Towards making unlabeled data never hurt. IEEE Trans. Pattern Anal. Mach. Intell. 37(1), 175–188 (2015)
Li, Y., Tsang, I.W., Kwok, J.T., Zhou, Z.: Convex and scalable weakly labeled SVMs. CoRR abs/1303.1271 (2013)
Nene, S.A., Nayar, S.K., Murase, H.: Columbia Object Image Library (COIL-100). Technical report, February 1996
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)
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)
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)
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)
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Bagattini, F., Cappanera, P., Schoen, F. (2018). A Simple and Effective Lagrangian-Based Combinatorial Algorithm for S\(^3\)VMs. In: Nicosia, G., Pardalos, P., Giuffrida, G., Umeton, R. (eds) Machine Learning, Optimization, and Big Data. MOD 2017. Lecture Notes in Computer Science(), vol 10710. Springer, Cham. https://doi.org/10.1007/978-3-319-72926-8_21
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