On-Line Algorithms in Machine Learning

Abstract

The areas of On-Line Algorithms and Machine Learning are both concerned with problems of making decisions about the present based only on knowledge of the past. Although these areas differ in terms of their emphasis and the problems typically studied, there are a collection of results in Computational Learning Theory that fit nicely into the 'on-line algorithms' framework. This survey article discusses some of the results, models, and open problems from Computational Learning Theory that seem particularly interesting from the point of view of on-line algorithms research. The emphasis in this article is on describing some of the simpler, more intuitive results, whose proofs can be given in their entirety. Pointers to the literature are given for more sophisticated versions of these algorithms.

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Document Details

Document Type
Technical Report
Publication Date
Jul 01, 1997
Accession Number
ADA330965

Entities

People

  • Avrim L. Blum

Organizations

  • Carnegie Mellon University

Tags

Communities of Interest

  • Autonomy
  • Human Systems

DTIC Thesaurus Topics

  • Algorithms
  • Computational Complexity
  • Computer Science
  • Delphi Method
  • Guarantees
  • Language
  • Learning
  • Literature
  • Machine Learning
  • Numbers
  • Operating Systems
  • Polynomials
  • Probability
  • Real Numbers
  • Sequences
  • Specialists
  • Standards

Readers

  • Parallel and Distributed Computing.
  • Systems Analysis and Design
  • Theoretical Analysis.

Technology Areas

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