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Abstract:
In high-dimensional spaces, its performance typically decays
due to the well-known "curse-of-dimensionality". A possible way to
approach this problem is by varying the "shape"of the weighting
kernel. In this work we suggest a new, data-driven method to
estimating the optimal kernel shape. Experiments using an
artificially generated data set and data from the UC Irvine
repository show the benefits of kernel shaping.
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