| PAC LEARNING WITH GENERALIZED SAMPLES AND AN APPLICATION TO STOCHASTIC GEOMETRY 1 (1991) | |||||||||||||||
Abstract | |||||||||||||||
| In this paper, we introduce an extension of the standard PAC model which allows the use of generalized samples. We view a generalized sample 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. We consider a specific application of the generalized model to a problem of curve reconstruction, and discuss some connections with a result from stochastic geometry. l'l'lis work was supported by the U.S. Army Researclh Office ut(ler Contract. 1)AAL03-86-K-0171, by the Depatrtnuent | |||||||||||||||
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