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PAC Learning with Generalized Samples and an Application to Stochastic Geometry

Author: S R Kulkarni; S K Mitter; J N Tsitsiklis; O Zeitouni; MASSACHUSETTS INST OF TECH CAMBRIDGE LAB FOR INFORMATION AND DECISION SYSTEMS.
Publisher: Ft. Belvoir Defense Technical Information Center JUN 1991.
Edition/Format:   eBook : EnglishView all editions and formats
Database:WorldCat
Summary:
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  Read more...
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Material Type: Internet resource
Document Type: Internet Resource
All Authors / Contributors: S R Kulkarni; S K Mitter; J N Tsitsiklis; O Zeitouni; MASSACHUSETTS INST OF TECH CAMBRIDGE LAB FOR INFORMATION AND DECISION SYSTEMS.
OCLC Number: 227911842
Notes: Sponsored in part by the National Science Foundation under Grant No., ECS-8552419.
Description: 19 p.

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.

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