Theoretical measures of relative performance of classifiers for high dimensional data with small sample sizes
We suggest a technique, related to the concept of 'detection boundary' that was developed by Ingster and by Donoho and Jin, for comparing the theoretical performance of classifiers constructed from small training samples of very large vectors. The resulting 'classification boundaries' are obtained for a variety of distance-based methods, including the support vector machine, distance-weighted discrimination and kth-nearest-neighbour classifiers, for thresholded forms of those methods, and for...[Show more]
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|Source:||Journal of the Royal Statistical Society Series B|
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