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Issue No.09 - September (1993 vol.15)
pp: 933-942
ABSTRACT
<p>An extension of the standard probably approximately correct (PAC) learning model that allows the use of generalized samples is introduced. A generalized sample is viewed as a pair consisting of a functional on the concept class together with the value obtained by the functional operating on the unknown concept. It appears that this model can be applied to a number of problems in signal processing and geometric reconstruction to provide sample size bounds under a PAC criterion. A specific application of the generalized model to a problem of curve reconstruction is considered, and some connections with a result from stochastic geometry are discussed.</p>
INDEX TERMS
probably approximately correct learning; PAC learning; generalized samples; stochastic geometry; signal processing; geometric reconstruction; sample size bounds; curve reconstruction; geometry; learning systems; signal processing; stochastic processes
CITATION
S.R. Kulkarni, S.K. Mitter, J.N. Tsitsiklis, O. Zeitouni, "PAC Learning with Generalized Samples and an Applicaiton to Stochastic Geometry", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol.15, no. 9, pp. 933-942, September 1993, doi:10.1109/34.232080
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