Two New Frameworks for Learning.

Abstract

This paper presents two new formal frameworks for learning. The first framework requires the learner to approximate an unknown function, given examples for the function as well as some background information on it. It is shown that this framework is no more powerful than a framework that allows the learner to see examples but not background information. The second framework explores learning in the sense of improving computational efficiency as opposed to acquiring an unknown concept or function. Specifically, the framework concerns the acquisition of heuristics for examples over problem domains of special structure. A theorem is proved identifying some conditions sufficient to allow the efficient acquisition of heuristics over the aforementioned class of domains.

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Document Details

Document Type
Technical Report
Publication Date
Nov 01, 1987
Accession Number
ADA187723

Entities

People

  • B. K. Natarajan

Organizations

  • Carnegie Mellon University

Tags

Communities of Interest

  • Autonomy

DTIC Thesaurus Topics

  • Acquisition
  • Algorithms
  • Alphabets
  • Artificial Intelligence
  • Computational Complexity
  • Cryptography
  • Efficiency
  • Hierarchies
  • Instructors
  • Intelligence Community
  • Learning
  • Machine Learning
  • Notation
  • Polynomials
  • Probability
  • Probability Distributions
  • Specifications

Readers

  • Artificial Intelligence
  • Operations Research