Scalable Kernel Systems
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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.
KeywordsSupport Vector Machine Base Point Kernel Weight Gaussian Process Regression Training Data Point
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- 1.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)Google Scholar
- 2.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)Google Scholar
- 3.Tresp, V.: The Bayesian Committee Machine. Neural Computation, Vol. 12 (2000)Google Scholar
- 4.Tresp, V.: Scaling Kernel-Based Systems to Large Data Sets. Data Mining and Knowledge Discovery, accepted for publicationGoogle Scholar
- 5.Wahba, G.: Spline models for observational data. Philadelphia: Society for Industrial and Applied Mathematics (1990)Google Scholar
- 6.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)Google Scholar