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Electronic Colloquium on Computational Complexity

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REPORTS > KEYWORD > LEARNING UNDER IRRELEVANT INFORMATION:
Reports tagged with learning under irrelevant information:
TR05-088 | 3rd August 2005
Jan Arpe

Learning Juntas in the Presence of Noise

The combination of two major challenges in machine learning is investigated: dealing with large amounts of irrelevant information and learning from noisy data. It is shown that large classes of Boolean concepts that depend on a small number of variables---so-called juntas---can be learned efficiently from random examples corrupted by random ... more >>>




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