Relations between exemplar-similarity and likelihood models of classification
Journal of Mathematical Psychology
An introduction to Kolmogorov complexity and its applications (2nd ed.)
An introduction to Kolmogorov complexity and its applications (2nd ed.)
Artificial Intelligence Review
The Simplicity and Power model for inductive inference
Artificial Intelligence Review
A NON-PARAMETRIC APPROACH TO SIMPLICITY CLUSTERING
Applied Artificial Intelligence
Private predictions on hidden Markov models
Artificial Intelligence Review
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We consider two models of unsupervised categorization, the simplicity model and the rational model. Their comparison is interesting because the models are based on proximal mathematical principles (minimum description length and Bayesian inference), but their implementation is very different (the simplicity model prefers groupings of similar items, while the rational model groupings which have higher utility). The models' predictions were assessed with a series of artificial datasets, such that each dataset was designed to reflect a simple intuition about human categorization processes. In the case of linearly separable categories, such that each category was composed of two subgroups, and in the case of non-linearly separable categories, the predictions of the simplicity model and rational diverged. Implications for future developments in unsupervised categorization are discussed.