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• W2158504911 abstract "We consider the problem of extracting informative exemplars from a data stream. Examples of this problem include exemplar-based clustering and nonparametric inference such as Gaussian process regression on massive data sets. We show that these problems require maximization of a submodular function that captures the informativeness of a set of exemplars, over a data stream. We develop an efficient algorithm, Stream-Greedy, which is guaranteed to obtain a constant fraction of the value achieved by the optimal solution to this NP-hard optimization problem. We extensively evaluate our algorithm on large real-world data sets." @default.
• W2158504911 created "2016-06-24" @default.
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• W2158504911 date "2010-06-21" @default.
• W2158504911 modified "2023-09-26" @default.
• W2158504911 title "Budgeted Nonparametric Learning from Data Streams" @default.
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