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A note on learning from multiple-instance examples
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DOI：10.1023/A:1007402410823.png)
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Abstract
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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 mole involved) reduction to the Statistical-Query model that results in a polynomial-time algorithm for learning axis-parallel rectangles with sample complexity (O) over tilde(d(2)r/epsilon(2)), saving roughly a factor of r over the results of Auer et al. (1997).
Keywords:
multiple-instance examples
classification noise
statistical queries
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