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Scalable Kernel Systems

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2130)

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.

Keywords

Support Vector Machine Base Point Kernel Weight Gaussian Process Regression Training Data Point 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  1. 1.Corporate TechnologySiemens AGMünchenGermany
  2. 2.Institute for Theoretical Computer ScienceTU GrazGrazAustria

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