Abstract
Empirical evidence indicates that the training time for the support vector machine (SVM) scales to the square of the number of training data points. In this paper, we introduce the Bayesian committee support vector machine (BC-SVM) and achieve an algorithm for training the SVM which scales linearly in the number of training data points. We verify the good performance of the BC-SVM using several data sets.
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References
Cristianini, N., Shawe-Taylor, J.: Support Vector Machines. Cambridge University Press, 2000
Joachims, T.: Making Large-scale Support Vector Machine Learning Practical. In: Schölkopf, B., Burges, C.J., Smola, A.J. (eds.), Advances in Kernel Methods-Support Vector Learning. MIT Press, 1998 pages 169–184
Solla, S.A., Leen, T.K., Müller, K.R. (eds.): Advances in Neural Information Processing Systems 12. MIT Press, 2000
Sollich, P.: Probabilistic Methods for Support Vector Machines. In: Leen, T.K., Müller, K.R. (eds.): Advances in Neural Information Processing Systems 12. MIT Press Solla et al. [3], pages 349–355, 2000
Tipping, M.E.: The Relevance Vector Machine. In: Leen, T.K., Müller, K.R. (eds.): Advances in Neural Information Processing Systems 12. MIT Press Solla et al. [3], 2000
Tresp, V.: A Bayesian Committee Machine. Neural Computation 12 (2000) 2719–2741
Tresp, V.: The Generalized Bayesian Committee Machine. In: Proceedings of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Boston, MA USA, 2000
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Schwaighofer, A., Tresp, V. (2001). The Bayesian Committee Support Vector Machine. In: Dorffner, G., Bischof, H., Hornik, K. (eds) Artificial Neural Networks — ICANN 2001. ICANN 2001. Lecture Notes in Computer Science, vol 2130. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44668-0_58
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DOI: https://doi.org/10.1007/3-540-44668-0_58
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