Scalable Kernel Systems

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.

• Type: Conference
• Year: 2001
• Where: ICANN
• Authors: Volker Tresp, Anton Schwaighofer
• Keywords: ICANN 2001, Kernel-based Systems, Kernel-based Systems Increases, Neural Networks, Training Data Points