Abstract
We present a new parallel and incremental Support Vector Machine (SVM) algorithm for the classification of very large datasets on graphics processing units (GPUs). SVM and kernel related methods have shown to build accurate models but the learning task usually needs a quadratic program so that this task for large datasets requires large memory capacity and long time. We extend a recent Least Squares SVM (LS-SVM) proposed by Suykens and Vandewalle for building incremental and parallel algorithm. The new algorithm uses graphics processors to gain high performance at low cost. Numerical test results on UCI and Delve dataset repositories showed that our parallel incremental algorithm using GPUs is about 70 times faster than a CPU implementation and often significantly faster (over 1000 times) than state-of-the-art algorithms like LibSVM, SVM-perf and CB-SVM.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Asuncion, A., Newman, D.J.: UCI Repository of Machine Learning Databases, http://archive.ics.uci.edu/ml/
Boser, B., Guyon, I., Vapnik, V.: A Training Algorithm for Optimal Margin Classifiers. In: Proc. of 5th ACM Annual Workshop on Computational Learning Theory, Pittsburgh, Pennsylvania, pp. 144–152 (1992)
Cauwenberghs, G., Poggio, T.: Incremental and Decremental Support Vector Machine Learning. In: Advances in Neural Information Processing Systems, vol. 13, pp. 409–415. MIT Press, Cambridge (2001)
Chang, C.-C., Lin, C.-J.: LIBSVM – A Library for Support Vector Machines (2003), http://www.csie.ntu.edu.tw/~cjlin/libsvm/
Delve, Data for evaluating learning in valid experiments (1996), http://www.cs.toronto.edu/~delve
Do, T.-N., Fekete, J.-D.: Large Scale Classification with Support Vector Machine Algorithms. In: Proc. of ICMLA 2007, 6th International Conference on Machine Learning and Applications, pp. 7–12. IEEE Press, Ohio (2007)
Do, T.-N., Poulet, F.: Classifying one Billion Data with a New Distributed SVM Algorithm. In: Proc. of RIVF 2006, 4th IEEE International Conference on Computer Science, Research, Innovation and Vision for the Future, Ho Chi Minh, Vietnam, Vietnam, pp. 59–66 (2006)
Domingos, P., Hulten, G.: A General Framework for Mining Massive Data Streams. Journal of Computational and Graphical Statistics 12(4), 945–949 (2003)
Dongarra, J., Pozo, R., Walker, D.: LAPACK++: a design overview of object-oriented extensions for high performance linear algebra. In: Proc. of Supercomputing 1993, pp. 162–171. IEEE Press, Los Alamitos (1993)
Fayyad, U., Piatetsky-Shapiro, G., Uthurusamy, R.: Summary from the KDD-03 Panel – Data Mining: The Next 10 Years. In: SIGKDD Explorations, vol. 5(2), pp. 191–196 (2004)
Fung, G., Mangasarian, O.: Incremental Support Vector Machine Classification. In: Proc. of the 2nd SIAM Int. Conf. on Data Mining SDM 2002 Arlington, Virginia, USA (2002)
Guyon, I.: Web Page on SVM Applications (1999), http://www.clopinet.com/isabelle/Projects/SVM/app-list.html
Joachims, T.: Training Linear SVMs in Linear Time. In: Proc. of the ACM SIGKDD Intl Conf. on KDD, pp. 217–226 (2006)
Mangasarian, O.: A Finite Newton Method for Classification Problems. Data Mining Institute Technical Report 01-11, Computer Sciences Department, University of Wisconsin (2001)
Mangasarian, O., Musicant, D.: Lagrangian Support Vector Machines. Journal of Machine Learning Research 1, 161–177 (2001)
NVIDIA® CUDATM, CUDA Programming Guide 1.1 (2007)
NVIDIA® CUDATM, CUDA CUBLAS Library 1.1 (2007)
Platt, J.: Fast Training of Support Vector Machines Using Sequential Minimal Optimization. In: Schoelkopf, B., Burges, C., Smola, A. (eds.) Advances in Kernel Methods – Support Vector Learning, pp. 185–208 (1999)
Poulet, F., Do, T.-N.: Mining Very Large Datasets with Support Vector Machine Algorithms. In: Camp, O., Filipe, J., Hammoudi, S. (eds.) Enterprise Information Systems V, pp. 177–184. Kluwer Academic Publishers, Dordrecht (2004)
Suykens, J., Vandewalle, J.: Least Squares Support Vector Machines Classifiers. Neural Processing Letters 9(3), 293–300 (1999)
Syed, N., Liu, H., Sung, K.: Incremental Learning with Support Vector Machines. In: Proc. of the 6th ACM SIGKDD Int. Conf. on KDD 1999, San Diego, USA (1999)
Tong, S., Koller, D.: Support Vector Machine Active Learning with Applications to Text Classification. In: Proc. of ICML 2000, the 17th Int. Conf. on Machine Learning, Stanford, USA, pp. 999–1006 (2000)
Vapnik, V.: The Nature of Statistical Learning Theory. Springer, New York (1995)
Wasson: Nvidia’s GeForce 8800 graphics processor, Technical report, PC Hardware Explored (2006)
Yu, H., Yang, J., Han, J.: Classifying Large Data Sets Using SVMs with Hierarchical Clusters. In: Proc. of the ACM SIGKDD Intl Conf. on KDD, pp. 306–315 (2003)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Do, TN., Nguyen, VH., Poulet, F. (2008). Speed Up SVM Algorithm for Massive Classification Tasks. In: Tang, C., Ling, C.X., Zhou, X., Cercone, N.J., Li, X. (eds) Advanced Data Mining and Applications. ADMA 2008. Lecture Notes in Computer Science(), vol 5139. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88192-6_15
Download citation
DOI: https://doi.org/10.1007/978-3-540-88192-6_15
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-88191-9
Online ISBN: 978-3-540-88192-6
eBook Packages: Computer ScienceComputer Science (R0)