Abstract
This paper presents a new decomposition algorithm for training support vector classifiers. The algorithm uses the analytical quadratic programming (QP) solver proposed in sequential minimal optimization (SMO) as its core solver. The new algorithm is featured by a nested buffer structure, which serves as a working set selection system. This system can achieve faster convergence by imposing restriction on the scope of working set selection. More efficient kernel cache utilization and more economical cache shape are additional benefits, which make the algorithm even faster. Experiments on various problems show that the new algorithm is 1.51 times as fast as LibSVM on average.
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References
Osuna, E.E., Freund, R., Girosi L, F.: Support Vector Machines: Training and Applications. Technical Report, MIT, March (1997)
Vapnik, V.N.: Vapnik: Estimation of Dependences Based on Empirical Data. Springer, New York (1982)
Platt. J.: Sequential Minimal Optimization: A Fast Algorithm for Training Support Vector Machines. Technical Report 98-14, Microsoft Research, Redmond, Washington (April 1998)
Joachims, T.: Making Large-Scale Support Vector Machine Learning Practical. In: Scholkopf, B., Burges, J.C., Smola, A.J. (eds.) Advances in Kernel Methods — Support Vector Learning, pp. 169–184. MIT Press, Cambridge (1999)
Keerthi, S.S., Shevade, S., Bhattacharyya, C., Murthy, K.: Improvements to Platt’s SMO Algorithm for SVM Classifier Design. Neural Computation 13, 637–649 (2001)
Chang, C.-C., Lin, C.-J.: LIBSVM: a Library for Support Vector Machines (Version 2.3) (2001), http://www.csie.ntu.edu.tw/~cjlin/libsvm/
Burges, C.J.C.: A Tutorial on Support Vector Machines for Pattern Recognition. Data Mining and Knowledge Discovery 2(2), 121–167 (1998)
Jianmin, L., Bo, M., Fuzong, L.: A New Strategy for Selecting Working Sets Applied in SMO. In: Proceedings of 16th International Conference on Pattern Recognition, pp. 427–430 (2002)
(June 2001), http://www.research.microsoft.com/~jplatt/adult.zip
(June 2001), http://www.research.microsoft.com/~jplatt/web.zip
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© 2004 Springer-Verlag Berlin Heidelberg
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Wu, X., Lu, W. (2004). Nested Buffer SMO Algorithm for Training Support Vector Classifiers. In: Yin, FL., Wang, J., Guo, C. (eds) Advances in Neural Networks – ISNN 2004. ISNN 2004. Lecture Notes in Computer Science, vol 3173. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28647-9_83
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DOI: https://doi.org/10.1007/978-3-540-28647-9_83
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22841-7
Online ISBN: 978-3-540-28647-9
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