Explorations of an incremental, Bayesian algorithm for categorization

  title={Explorations of an incremental, Bayesian algorithm for categorization},
  author={John R. Anderson and Michael Matessa},
  journal={Machine Learning},
An incremental categorization algorithm is described which, at each step, assigns the next instance to the most probable category. Probabilities are estimated by a Bayesian inference scheme which assumes that instances are partitioned into categories and that within categories features are displayed independently and probabilistically. This algorithm can be shown to be an optimization of an ideal Bayesian algorithm in which predictive accuracy is traded for computational efficiency. The… CONTINUE READING
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