Abstract
This paper develops a fast and accurate algorithm for training transductive SVMs classifiers, which utilizes the classification information of unlabeled data in a progressive way. For improving the generalization accuracy further, we employ three important criteria to enhance the algorithm, i.e. confidence evaluation, suppression of labeled data, stopping with stabilization. Experimental results on several real world datasets confirm the effectiveness of these criteria and show that the new algorithm can reach to comparable accuracy as several state-of-the-art approaches for training transductive SVMs in much less training time.
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References
Breiman, L.: Bagging Predictors. Mach. Learn. 24, 123–140 (1996)
Chapelle, O., Zien, A.: Semi-supervised Classification by Low Density Separation. In: Proc. 10th Int. Workshop on AI&Statistics, pp. 57–64 (2005)
Chi, M.M., Bruzzone, L.: A Novel Transductive SVM for Semi-supervised Classification of Remote Sensing Images. In: 11th SPIE Int. Symposium on Remote Sensing, pp. 153–164 (2005)
Joachims, T.: Transductive Inference for Text Classification using Support Vector Machine. In: 16th Int. Conf. on Mach. Learn., pp. 200–209 (1999)
Joachims, T.: Making Large-scale SVM Learning Practical. In: Schölkopf, B., et al. (eds.) Advances in Kernel Methods: Support Vector Learning, pp. 169–184. MIT Press, Cambridge (1999)
Li, M.K., Sethi, I.K.: Confidence-based Classifier Design. Pattern Recognition 39, 1230–1240 (2006)
Liu, H., Huang, S.T.: Fuzzy Transductive Support Vector Machines for Hypertext Classification. Int. J. of Uncertainty, Fuzziness and Knowledge-based Systems 12, 21–36 (2004)
Sindhwani, V., Keerthi, S., Chapelle, O.: Deterministic Annealing for Semi-supervised Kernel Machines. In: Proc. 23rd Int. Conf. on Mach. Learn., pp. 841–848 (2006)
Vapnik, V.: The Nature of Statistical Learning Theory, 2nd edn. Springer, Berlin (1995)
Wang, Y., Huang, S.T.: Training TSVM with the Proper Number of Positive Samples. Pattern Recognition Letters 26, 2187–2194 (2005)
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© 2007 Springer Berlin Heidelberg
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Wang, L., Jia, H., Sun, S. (2007). A Fast and Accurate Progressive Algorithm for Training Transductive SVMs. In: Liu, D., Fei, S., Hou, Z., Zhang, H., Sun, C. (eds) Advances in Neural Networks – ISNN 2007. ISNN 2007. Lecture Notes in Computer Science, vol 4493. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72395-0_63
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DOI: https://doi.org/10.1007/978-3-540-72395-0_63
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-72394-3
Online ISBN: 978-3-540-72395-0
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