Abstract
In this paper we present a new algorithm to speed up the training time of Support Vector Machines (SVM). SVM has some important properties like solid mathematical background and a better generalization capability than other machines like for example neural networks. On the other hand, the major drawback of SVM occurs in its training phase, which is computationally expensive and highly dependent on the size of input data set. The proposed algorithm uses a data filter to reduce the input data set to train a SVM. The data filter is based on an induction tree which effectively reduces the training data set for SVM, producing a very fast and high accuracy algorithm. According to the results, the algorithm produces results in a faster way than existing SVM implementations (SMO, LIBSVM and Simple-SVM) with similar accurateness.
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References
Vapnik, V.: Statistical Learning Theory. Wiley, New York (1998)
Platt, J.C.: Fast Training of Support Vector Machines Using Sequential Minimal Optimization. In: Scholkopf, B., Burges, C.J.C., Smola, A.J. (eds.) Advances in Kernel Methods-Support Vector Learning, pp. 185–208. MIT Press (1998)
Cervantes, J., Li, X., Yu, W., Li, K.: Support vector machine classification for large data sets via minimum enclosing ball clustering. Neurocomputing 71(4-6), 611–619 (2008)
Yu, H., Yang, J., Han, J.: Classifying Large Data Sets Using SVM with Hierarchical Clusters. In: Proc. of the ACM SIGKDD Intl. Conf. on Knowledge, pp. 306–315 (2003)
Dong, J., Krzyzak, A., Suen, C.Y.: Fast SVM Training Algorithm with Decomposition on Very Large Data Sets. IEEE Trans. Pattern Anal. Mach. Intell. 27(4), 603–618 (2005)
López, J., Barbero, Á., Dorronsoro, J.R.: Simple Clipping Algorithms for Reduced Convex Hull SVM Training. In: Corchado, E., Abraham, A., Pedrycz, W. (eds.) HAIS 2008. LNCS (LNAI), vol. 5271, pp. 369–377. Springer, Heidelberg (2008)
Wu, C., Wang, X., Bai, D., Zhang, H.: Fast SVM incremental learning based on the convex hulls algorithm. In: Proc. of the Intl. Conf. on Computational Intelligence and Security, pp. 249–252. IEEE Computer Society (2008)
Mavroforakis, M.E., Sdralis, M., Heodoridis, S.: A geometric nearest point algorithm for the efficient solution of the SVM classification task. IEEE Trans. Neural Networks 18, 1545–1549 (2007)
Bottou, L., Lin, C.J.: Support Vector Machine Solvers. In: Bottou, L., Chapelle, O., DeCoste, D., Weston, J. (eds.) Large Scale Kernel Machines, pp. 1–28. MIT Press (2007)
Chambers, R.L., Skinner, C.J (eds.): Analysis of Survey Data. Wiley (2003) ISBN 0-471-89987-9
Schohn, G., Cohn, D.: Less is more: Active learning with support vector machines. In: Proc. Intl. Conf. on Machine Learning, pp. 839–846 (2000)
Burges, C.J.: A Tutorial on Support Vector Machines for Pattern Recognition. Data Min. Knowl. Discov. 2(2), 121–167 (1998)
Quinlan, J.R.: Improved use of continuous attributes in c4.5. J. Artificial Intell. Res. 4, 77–90 (1996)
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Cervantes, J., López, A., García, F., Trueba, A. (2011). A Fast SVM Training Algorithm Based on a Decision Tree Data Filter. In: Batyrshin, I., Sidorov, G. (eds) Advances in Artificial Intelligence. MICAI 2011. Lecture Notes in Computer Science(), vol 7094. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25324-9_16
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DOI: https://doi.org/10.1007/978-3-642-25324-9_16
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