Analyzing human feature learning as nonparametric Bayesian inference

  title={Analyzing human feature learning as nonparametric Bayesian inference},
  author={Joseph L. Austerweil and Thomas L. Griffiths},
Almost all successful machine learning algorithms and cognitive models require powerful representations capturing the features that are relevant to a particular problem. We draw on recent work in nonparametric Bayesian statistics to define a rational model of human feature learning that forms a featural representation from raw sensory data without pre-specifying the number of features. By comparing how the human perceptual system and our rational model use distributional and category… CONTINUE READING


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