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# Miles Crosskey : Spectral bounds on empirical operators

Many machine learning algorithms are based upon estimating eigenvalues and eigenfunctions of certain integral operators. In practice, we have only finitely many randomly drawn points. How close are the eigenvalues and eigenfunctions of the finite dimensional matrix we construct in comparison to the infinite dimensional integral operator? In what way can we say these two are close if they do not even operate on the same spaces? To answer these questions, I will be showing some results from a paper "On Learning with Integral Operators" by Rosasco, Belkin, and De Vito.

**Category**: Graduate/Faculty Seminar**Duration**: 01:34:52**Date**: April 9, 2010 at 4:25 PM**Views**: 109-
**Tags:**seminar, Graduate/faculty Seminar

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