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<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>
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

S. Kulkarni, O. Zeitouni, S. Mitter and J. Tsitsiklis, "PAC Learning with Generalized Samples and an Applicaiton to Stochastic Geometry," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 15, no. , pp. 933-942, 1993.
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