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Learning from Infinite Data in Finite Time

By Pedro Domingos and Geoff Hulten


We propose the following general method for scaling learning algorithms to arbitrarily large data sets. Consider the model M ~n learned by the algorithm using n i examples in step i (~n = (n 1 ; : : : ; nm )), and the model M1 that would be learned using infinite examples. Upper-bound the loss L(M ~n ; M1 ) between them as a function of ~n, and then minimize the algorithm's time complexity f(~n) subject to the constraint that L(M1 ; M ~n ) be at most with probability at most . We apply this method to the EM algorithm for mixtures of Gaussians. Preliminary experiments on a series of large data sets provide evidence of the potential of this approach

Year: 2001
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