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Nearest Hyperdisk Methods for High-Dimensional Classification

Nearest Hyperdisk Methods for High-Dimensional Classification

This video was recorded at 25th International Conference on Machine Learning (ICML), Helsinki 2008. In high-dimensional classification problems it is infeasible to include enough training samples to cover the class regions densely. Irregularities in the resulting sparse sample distributions cause local classifiers such as Nearest Neighbors (NN) and kernel methods to have irregular decision boundaries. One solution is to "fill in the holes" by building a convex model of the region spanned by the training samples of each class and classifying examples based on their distances to these approximate models. Methods of this kind based on affine and convex hulls and bounding hyperspheres have already been studied. Here we propose a method based on the bounding hyperdisk of each class -- the... Show More

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