### Anytime interval-valued outputs for kernel machines: fast support vector machine classification via distance geometry

Classifying M query examples using a support vector machine containing L support vectors traditionally requires exactly M * L kernel computations.

We introduce a computational geometry method for which classification cost becomes roughly proportional to each query's difficulty (e.g. distance from the discriminant hyperplane). It produces exactly the same classifications, while typically requiring vastly fewer kernel computations.

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