Abstract
Kernel-based systems are currently very popular approaches to supervised learning. Unfortunately, the computational load for training kernel-based systems increases drastically with the number of training data points. Recently, a number of approximate methods for scaling kernel-based systems to large data sets have been introduced. In this paper we investigate the relationship between three of those approaches and compare their performances experimentally.
This is a preview of subscription content, log in via an institution to check access.
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
Lee, Y.-J. and Mangasarian, O. L.: RSVM: Reduced Support Vector Machines. Data Mining Institute Technical Report 00-07, Computer Sciences Department, University of Wisconsin (2000)
Smola, A.J. and Bartlett, P.: Sparse Greedy Gaussian Process Regression. In: T. K. Leen, T. G. Diettrich and V. Tresp, (eds.): Advances in Neural Information Processing Systems 13 (2001)
Tresp, V.: The Bayesian Committee Machine. Neural Computation, Vol. 12 (2000)
Tresp, V.: Scaling Kernel-Based Systems to Large Data Sets. Data Mining and Knowledge Discovery, accepted for publication
Wahba, G.: Spline models for observational data. Philadelphia: Society for Industrial and Applied Mathematics (1990)
Williams, C.K.I. and Seeger, M.: Using the Nyström Method to Speed up Kernel Machines. In: T. K. Leen, T. G. Diettrich and V. Tresp, (eds.): Advances in Neural Information Processing Systems 13 (2001)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2001 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Tresp, V., Schwaighofer, A. (2001). Scalable Kernel Systems. 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_41
Download citation
DOI: https://doi.org/10.1007/3-540-44668-0_41
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-42486-4
Online ISBN: 978-3-540-44668-2
eBook Packages: Springer Book Archive