Stochastic Foundations for the Case-Driven Acquisition of Classification Rules

  • Megan Vazey
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4248)

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.

Keywords

Knowledge Acquisition Group Decision Support Systems Colla-borative Tagging Folksonomies Knowledge Based Systems Machine Learning Knowledge Discovery in Databases Case Based Reasoning Ripple Down Rules Expert Systems 

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References

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    Kang, B.H., Lee, K., Kim, W., Preston, P., Compton, P.: Evaluation of Multiple Classification Ripple Down Rules. In: Eleventh Workshop on Knowledge Acquisition, Modeling and Management (KAW), Banff, Alberta, Canada, April 18-23 (1998)Google Scholar
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    Golder, S.A., Huberman, B.A.: The Structure of Collaborative Tagging Systems. HP Labs (2003)Google Scholar
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    Kang, B., Compton, P., Preston, P.: Multiple Classification Ripple Down Rules: Evaluation and Possibilities. In: Proceedings of the 9th AAAI-Sponsored Banff Knowledge Acquisition for Knowledge-Based Systems Workshop, Banff, Canada, University of Calgary (1995)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Megan Vazey
    • 1
  1. 1.Department of Computing, Division of Information and Communication SciencesMacquarie UniversitySydneyAustralia

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