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Open Access Publications from the University of California

Scholarly Works (2 results)

  • Article
  • Peer Reviewed
Proceedings of the Annual Meeting of the Cognitive Science Society, Volume 20 (1998)
This paper examines the naive Bayesian model and extensions of it to account for the effects of base rate neglect and inverse base rates. These are human categorization phenomena in which base rate information appears to be ignored. The naive Bayesian classifier accounts for a subset of the phenomena observed in base rate experiments. An extension to the model is examined that uses structure in the data sets resulting from features shared between categories.
    Cover page: Naive Bayesian Accounts of Base Rate Effects in Human Categorization
    • Article
    • Peer Reviewed
    ICS Technical Reports (1985)

    In this paper we describe GLAUBER, an AI system that models the scientific discovery of qualitative empirical laws. We have tested the system on data from the history of early chemistry, and it has rediscovered such concepts as acids, alkalis, and salts, as well as laws relating these concepts. After discussing GLAUBER we examine the program's relation to other discovery systems, particularly methods for conceptual clustering and language acquisition.

      Cover page: Discovering qualitative empirical laws
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