Abstract
The well-known bounds on the generalizationability of learning machines, based on the Vapnik–Chernovenkis (VC) dimension,are very loose when applied to Support Vector Machines (SVMs).In this work we evaluate the validity of the assumption that these bounds are,nevertheless, good indicators of the generalization ability of SVMs.We show that this assumption is, in general, true and assessits correctness, in a statistical sense, on several pattern recognition benchmarks throughthe use of the bootstrap technique.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Anguita, D., Boni, A. and Ridella, S.: Support Vector Machines: a comparison of some kernel functions, Proc. of the 3rd Int. Symp. on Soft Computing, Genova, Italy, (1999).
Anguita, D., Boni, A., Chirico, M., Giudici, F., Scapolla, A. M. and Parodi, G.: High performance neurocomputing: Industrial and medical applications of the RAIN system, In: P. Sloot, M. Bubak, B. Hertzberger (eds.), Proc. of HPCN Europe 1998, Amsterdam, The Netherlands, pp. 34-43, Lecture Notes in Computer Science 1401, Springer-Verlag, Berlin, Germany, 1998.
Bartlett, P. and Shawe-Taylor, J.: Generalization performance of Support Vector Machines and other pattern classifiers, In: Schölkopf, B., Burges, C., Smola, A. (eds.), Advances in Kernel Methods-Support Vector Learning. MIT Press, Cambridge, MA, 1999.
Blake, C., Keogh, E. and Merz, C. J.: UCI repository of machine learning databases, http://www.ics.uci.edu/ mlearn/MLRepository.html, Irvine, CA, University of California, Department of Information and Computer Science, 1998.
Burges, C. J. C.: A tutorial on Support Vector Machines for pattern recognition, Data Mining and Knowledge Discovery 2(2) (1998), 1-47.
Cortes, C. and Vapnik, V. N.: Support Vector Networks, Machine Learning 20 (1995), 1-25.
Cristianini, N., Campbell, C. and Shawe-Taylor, J.: Dynamically adapting kernels in Support Vector Machines, NeuroCOLT2 Technical Report Series, Royal Holloway College, University of London, UK, NC2-TR-1998-017, 1998.
Dietterich, T. G.: Comparing supervised classification learning algorithms, Neural Computation 10 (1998), 1895-1923.
Efron, B. and Tibshirani, R. J.: An Introduction to the Bootstrap, Chapman and Hall, New York, USA, 1993.
Geist, A., Beguelin, A., Dongarra, J., Jiang,W., Mancheck, R. and Sunderam, V.: PVM: Parallel Virtual Machine, MIT Press, Cambridge, MA, 1994.
Hearst, M. A.: Support Vector Machines, IEEE Intelligent Systems 13(4) (1998), 18-28.
Jain, A. K., Dubes, R. C. and Chen, C. C.: Bootstrap techniques for error estimation, IEEE Trans. on PAMI 9(5) (1987), 628-633.
Torres Moreno, J. M. and Gordon, M. B.: Characterization of the sonar signals benchmark, Neural Processing Letters 7 (1998), 1-4.
Vapnik, V. N.: The Nature of Statistical Learning Theory, John Wiley and Sons, New York, USA, 1995.
Vapnik, V. N.: Statistical Learning Theory, Springer, New York, USA, 1998.
Author information
Authors and Affiliations
Rights and permissions
About this article
Cite this article
Anguita, D., Boni, A. & Ridella, S. Evaluating the Generalization Ability of Support Vector Machines through the Bootstrap. Neural Processing Letters 11, 51–58 (2000). https://doi.org/10.1023/A:1009636300083
Issue Date:
DOI: https://doi.org/10.1023/A:1009636300083