Principle of parsimony or the Occam's razor is a key principle to improve the generalization performance in pattern classification. The principle is essentially equivalent to reducing the variance of a classifier at the cost of increased boundary bias. In this paper, we provide a quantitative way to express this principle in terms of the outcome of a classifier instead of explicitly regularizing the model complexity in terms of model parameters. We then use this principle to design a new kernel machine and a modified $k$-nearest neighbor algorithm. Experimentally we validate the performance of these two classifiers over real-life datasets. We also discuss the relationship of the proposed kernel machine with several other existing kernel machines.
By: Jayanta Basak
Published in: RI09003 in 2009
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