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Abstract:
Greedy approximation algorithms have been frequently used to
obtain sparse solutions to learning problems. In this paper, we
present a general greedy algorithm for solving a class of convex
optimization problems. We derive a bound on the rate of
approximation for this algorithm, and show that our algorithm
includes a number of earlier studies as special cases.
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