PAC Learning with Generalized Samples and an Application to Stochastic Geometry

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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Document Details

Document Type
Technical Report
Publication Date
Jun 01, 1991
Accession Number
ADA459600

Entities

People

  • J. N. Tsitsiklis
  • O. Zeitouni
  • Sanjoy K. Mitter
  • Shrinivas Kulkarni

Organizations

  • Massachusetts Institute of Technology

Tags

Communities of Interest

  • C4I

DTIC Thesaurus Topics

  • Artificial Intelligence
  • Computations
  • Construction
  • Contracts
  • Convex Sets
  • Coordinate Systems
  • Curvature
  • Geometry
  • Grids
  • Image Processing
  • Learning
  • Probability
  • Real Numbers
  • Sampling
  • Standards
  • Statistical Samples
  • Theorems

Fields of Study

  • Mathematics

Readers

  • Computer Science.
  • Computer Vision.
  • Mathematical Modeling and Probability Theory.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Learning Algorithms