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The Tradeoffs of Large Scale Learning

Advances in Neural Information Processing Systems, NIPS Foundation (http://books.nips.cc) (2008), pp. 161-168

Abstract

This contribution develops a theoretical framework that takes into account the effect of approximate optimization on learning algorithms. The analysis shows distinct tradeoffs for the case of small-scale and large-scale learning problems. Small-scale learning problems are subject to the usual approximation–estimation tradeoff. Large-scale learning problems are subject to a qualitatively different tradeoff involving the computational complexity of the underlying optimization algorithms in non-trivial ways.

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