A General Method for Scaling Up Machine Learning Algorithms and its Application to Clustering
Published 28 June 2001
Pedro Domingos, Geoff Hulten
Citations171
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Abstract
We propose to scale learning algorithms to arbitrarily large databases by the following method. First derive an upper bound for the learner's loss as a function of the number of examples used in each step of the algorithm.
Keywords
Computer Science