Stochastic foundations for the case-driven acquisition of classification rules

Megan Vazey*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contribution

2 Citations (Scopus)

Abstract

A predictive mathematical model is presented for the expected case-driven transfer of classification rules. Key insights are offered for Knowledge Acquisition in expert systems, machine learning, artificial intelligence, ontology, and folksomonies.

Original languageEnglish
Title of host publicationManaging Knowledge in a World of Networks - 15th International Conference, EKAW 2006, Proceedings
PublisherSpringer, Springer Nature
Pages43-50
Number of pages8
Volume4248 LNAI
ISBN (Print)3540463631, 9783540463634
Publication statusPublished - 2006
Event15th International Conference on Knowledge Engineering and Knowledge Management, EKAW 2006 - Podebrady, Czech Republic
Duration: 2 Oct 20066 Oct 2006

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4248 LNAI
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other15th International Conference on Knowledge Engineering and Knowledge Management, EKAW 2006
CountryCzech Republic
CityPodebrady
Period2/10/066/10/06

Keywords

  • Case based reasoning
  • Collaborative tagging
  • Expert systems
  • Folksonomies
  • Group decision support systems
  • Knowledge acquisition
  • Knowledge based systems
  • Knowledge discovery in databases
  • Machine learning
  • Ripple down rules

Fingerprint Dive into the research topics of 'Stochastic foundations for the case-driven acquisition of classification rules'. Together they form a unique fingerprint.

  • Cite this

    Vazey, M. (2006). Stochastic foundations for the case-driven acquisition of classification rules. In Managing Knowledge in a World of Networks - 15th International Conference, EKAW 2006, Proceedings (Vol. 4248 LNAI, pp. 43-50). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4248 LNAI). Springer, Springer Nature.