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Learning to predict the leave-one-out error of kernel based classifiers

: Tsuda, K.; Rätsch, G.; Mika, S.; Müller, K.-R.

Dorffner, G.:
Artificial neural networks : International conference,Vienna, Austria, August 21 - 25, 2001 ; proceedings ICANN 2001
Berlin: Springer, 2001 (Lecture Notes in Computer Science 2130)
ISBN: 3-540-42486-5
International Conference on Artificial Neural Networks (ICANN) <11, 2001, Vienna>
Conference Paper
Fraunhofer FIRST ()

We propose an algorithm to predict the leave-one-out (LOO) error for kernel based classifiers. To achieve this goal with computational efficiency, we cast the LOO error approximation task into a classification problem. This means that we need to learn a classification of whether or not a given training sample - if left out of the data set - would be misclassified. For this learning task, simple data dependent features are proposed, inspired by geometrical intuition. Our approach allows to reliably select a good model as demonstrated in simulations on Support Vector and Linear Programming Machines. Comparisons to existing learning theoretical bounds, e,g. the span bound, are given for various model selection scenarios.