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Abstract: We describe a simple reduction from the problem of PAC-learning from multiple-instance examples to that of PAC-learning with one-sided random classification noise. Thus, all concept classes learnable with one-sided noise, which includes all concepts learnable in the usual 2-sided random noise model plus others such as the parity function, are learnable from multiple-instance examples. We also describe a more efficient (and somewhat technically more involved) reduction to the Statistical-Query... (Update)
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BibTeX entry: (Update)
A. Blum and A. Kalai. A Note on Learning from Multiple-Instance Examples. To appear in Machine Learning, 1998. http://citeseer.ist.psu.edu/blum98note.html More
@article{ blum98note,
author = "Avrim Blum and Adam Kalai",
title = "A Note on Learning from Multiple-Instance Examples",
journal = "Machine Learning",
volume = "30",
number = "1",
pages = "23-29",
year = "1998",
url = "citeseer.ist.psu.edu/blum98note.html" }
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22 On learning from multi-instance examples: Empirical evaluati.. - Auer - 1997
19 Approximating hyper-rectangles: Learning and pseudo-random s.. - Auer, Long et al. - 1997
18 PAC learning axis-aligned rectangles with respect to product.. (context) - Long, Tan - 1996
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