WebWatcher: Machine Learning and Hypertext

Abstract

This paper describes the first implementation of WebWatcher, a Learning Apprentice for the World Wide Web. We also explore the possibility of extracting information from the structure of hypertext. We introduce an algorithm which identifies pages that are related to a given page using only hypertext structure. We motivate the algorithm by using the Minimum Description Length principle.

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Document Details

Document Type
Technical Report
Publication Date
May 29, 1995
Accession Number
ADA635877

Entities

People

  • Dayne Freitag
  • Robert Armstrong
  • Thorsten Joachims
  • Tom M. Mitchell

Organizations

  • Carnegie Mellon University

Tags

Communities of Interest

  • Autonomy

DTIC Thesaurus Topics

  • Abstracts
  • Algorithms
  • Bayes Theorem
  • Computer Science
  • Electronic Mail
  • Hypertext
  • Information Operations
  • Information Retrieval
  • Language
  • Learning
  • Machine Learning
  • Mathematics
  • Natural Languages
  • Probability
  • Probability Distributions
  • World Wide Web

Fields of Study

  • Computer science

Readers

  • Applied Combinatorial Optimization and Logic Circuit Design.
  • Database Systems and Applications
  • STEM Education

Technology Areas

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