Learning to Predict the Leave-One-Out Error of Kernel Based Classifiers

Authors:
Koji Tsuda;Gunnar Rätsch;Sebastian Mika;Klaus-Robert Müller
Affiliations:
-;-;-;-
Venue:
ICANN '01 Proceedings of the International Conference on Artificial Neural Networks
Year:
2001

Citing 4
Cited 3

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Abstract

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.