• Corpus ID: 6176762

Bayesian Classification (AutoClass): Theory and Results

  title={Bayesian Classification (AutoClass): Theory and Results},
  author={Peter C. Cheeseman and John C. Stutz},
  booktitle={Advances in Knowledge Discovery and Data Mining},
We describe AutoClass an approach to unsupervised classi cation based upon the classical mixture model supplemented by a Bayesian method for determining the optimal classes We include a moderately detailed exposition of the mathematics behind the AutoClass system We emphasize that no current unsupervised classi cation system can produce maximally useful results when operated alone It is the interaction between domain experts and the machine searching over the model space that generates new… 

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  • Jun-Hao WenC. LingQiang Yang
  • Computer Science
    Proceedings of the 2003 International Conference on Machine Learning and Cybernetics (IEEE Cat. No.03EX693)
  • 2003
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Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper

Vibratory power unit for vibrating conveyers and screens comprising an asynchronous polyphase motor, at least one pair of associated unbalanced masses disposed on the shaft of said motor, with the

An Improved Automatic Classi cation

  • 1994