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Pattern Recognition and Valiant's Learning Framework
February 1993 (vol. 15 no. 2)
pp. 145-155

The computational learning approach shows that the concept descriptions acquired from examples are approximately correct with a degree of probability that grows with the size of the training sample. The same problem has also been widely investigated in the field of pattern recognition under a variety of problem settings. Some of the results obtained in both fields are surveyed and compared, and the limits of their applicability are analyzed. Moreover, new and tighter bounds for the growth function of some classes of Boolean formulas are presented.

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Index Terms:
Valiant's learning framework; computational learning approach; concept descriptions; probability; training sample; pattern recognition; growth function; Boolean formulas; Boolean functions; learning (artificial intelligence); pattern recognition; probability
Citation:
L. Saitta, F. Bergadano, "Pattern Recognition and Valiant's Learning Framework," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 15, no. 2, pp. 145-155, Feb. 1993, doi:10.1109/34.192486
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