In form of the support vector machine and Gaussian processes, 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. Recently, the reduced rank approximation and the BCM approximation have been introduced as approximate methods for scaling kernel-based systems to large data sets. In this paper we investigate the relationship between both approaches and compare their performances experimentally.

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In  Artificial Neural Networks – ICANN 2001

Publisher  Springer Verlag

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