Some Results on Learning

Abstract

This paper presents some formal results on learning. In particular, it concerns algorithms that learn sets and functions from examples. We seek conditions necessary and sufficient for learning over a range of probabilistic models for such algorithms. This paper concerns algorithms that learn sets and functions from examples for them. The motivation behind the study is a need to better understand the class of problems known as 'concept learning problems' in the Artificial Intelligence literature.

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Document Details

Document Type
Technical Report
Publication Date
Feb 01, 1989
Accession Number
ADA210591

Entities

People

  • B. K. Natarajan

Organizations

  • Carnegie Mellon University

Tags

Communities of Interest

  • Autonomy

DTIC Thesaurus Topics

  • Abstracts
  • Algorithms
  • Artificial Intelligence
  • Availability
  • Classification
  • Computer Science
  • Fragmentation
  • Inequalities
  • Learning
  • Literature
  • Machine Learning
  • Models
  • Polynomials
  • Probabilistic Models
  • Probability
  • Probability Distributions
  • Security

Fields of Study

  • Computer science

Readers

  • Agent-Based Social Robotics and Mobile-Assisted Learning in Virtual Environments.
  • Graph Algorithms and Convex Optimization.

Technology Areas

  • AI & ML
  • AI & ML - Machine Learning Algorithms
  • AI & ML - Neural Networks