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A General Dimension for Query Learning
"... We introduce a new combinatorial dimension that characterizes the number of queries needed to learn, no matter what set of queries is used. This new dimension generalizes previous dimensions providing upper and lower bounds on the query complexity for all sorts of queries, and not for just examp ..."
Abstract
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Cited by 4 (2 self)
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We introduce a new combinatorial dimension that characterizes the number of queries needed to learn, no matter what set of queries is used. This new dimension generalizes previous dimensions providing upper and lower bounds on the query complexity for all sorts of queries, and not for just example-based queries as in previous works. Moreover, the new characterization is not only valid for exact learning but also for approximate learning. We present several Results from sections 4 and 5 were presented at COLT/EUROCOLT 2001 [4]; results from sections 7 and 8 were presented at ALT 2002 [24].
A General Dimension for Query Learning
, 2002
"... We introduce a new combinatorial dimension that characterizes the number of queries needed to learn, no matter what set of queries is used. This new dimension generalizes previous dimensions providing upper and lower bounds on the query complexity for all sorts of queries, and not for just examp ..."
Abstract
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Cited by 1 (0 self)
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We introduce a new combinatorial dimension that characterizes the number of queries needed to learn, no matter what set of queries is used. This new dimension generalizes previous dimensions providing upper and lower bounds on the query complexity for all sorts of queries, and not for just example-based queries as in previous works. Moreover, the new characterization is not only valid for exact learning but also for approximate learning. We present several
