The Complexity of Learning from a Mixture of Labeled and Unlabeled Examples

Abstract

The learning of a pattern classification rule rests on acquiring information to constitute a decision rule that is close to the optimal Bayes rule. Among the various ways of conveying information, showing the learner examples from the different classes is an obvious approach and ubiquitous in the pattern recognition field. Basically there are two types of examples: labeled in which the learner is provided with the correct classification of the example and unlabeled in which this classification is missing. Driven by the reality that often unlabeled examples are plentiful whereas labeled examples are difficult or expensive to acquire we explore the tradeoff between labeled and unlabeled sample complexities (the number of examples required to learn to within a specified error), specifically getting a quantitative measure of the reduction in the labeled sample complexity as a result of introducing unlabeled examples.

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Document Details

Document Type
Technical Report
Publication Date
Apr 01, 1994
Accession Number
ADA356661

Entities

People

  • Joel E. Ratsaby

Organizations

  • University of Pennsylvania

Tags

Communities of Interest

  • C4I
  • Human Systems

DTIC Thesaurus Topics

  • Algorithms
  • Artificial Intelligence
  • Bayesian Networks
  • Computational Science
  • Estimators
  • Information Science
  • Kernel Functions
  • Learning
  • Machine Learning
  • Maximum Likelihood Estimation
  • Neural Networks
  • Probability
  • Probability Distributions
  • Random Variables
  • Self Organizing Systems
  • Three Dimensional
  • Two Dimensional

Readers

  • Artificial Intelligence
  • Educational Psychology
  • Statistical inference.

Technology Areas

  • AI & ML
  • AI & ML - Neural Networks