• Corpus ID: 6176762

Bayesian Classification (AutoClass): Theory and Results

@inproceedings{Cheeseman1996BayesianC,
  title={Bayesian Classification (AutoClass): Theory and Results},
  author={Peter C. Cheeseman and John C. Stutz},
  booktitle={Advances in Knowledge Discovery and Data Mining},
  year={1996}
}
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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TLDR
A Bayesian approach to the unsupervised discovery of classes in a set of cases, sometimes called finite mixture separation or clustering, which allows direct comparison of alternate density functions that differ in number of classes and/or individual class density functions.

Bayesian Classification with Correlation and Inheritance

The task of inferring a set of classes and class descriptions most likely to explain a given data set can be placed on a firm theoretical foundation using Bayesian statistics. Within this framework,

A Bayesian classification of the IRAS LRS Atlas

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The availability of a reclassification of the IRAS LRS Atlas of spectra using a new Bayesian classification procedure (AutoClass) is announced, which includes many of the previously known LRS classes.

Promise of Bayesian Inference for Astrophysics

The "frequentist" approach to statistics, currently domi­ nating statistical practice in astrophysics, is compared to the historically older Bayesian approach, which is now growing in popularity in

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This research note shows the results of applying a new massively parallel version of the automatic classification program (AutoClass IV) to a particular Landsat/TM image. The previous results for

Bayesian inference in statistical analysis

TLDR
This chapter discusses Bayesian Assessment of Assumptions, which investigates the effect of non-Normality on Inferences about a Population Mean with Generalizations in the context of a Bayesian inference model.

対数正規分布(Lognormal Distribution)のあてはめについて

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Statistical analysis of finite mixture distributions

TLDR
This course discusses Mathematical Aspects of Mixtures, Sequential Problems and Procedures, and Applications of Finite Mixture Models.

Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper

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An Improved Automatic Classi cation

  • 1994