Stephanie Sage

ICML Conference 1999 Conference Paper

Tractable Average-Case Analysis of Naive Bayesian Classifiers

• Pat Langley
• Stephanie Sage

UAI Conference 1994 Conference Paper

Induction of Selective Bayesian Classifiers

• Pat Langley
• Stephanie Sage

In this paper, we examine previous work on the naive Bayesian classifier and review its limitations, which include a sensitivity to correlated features. We respond to this problem by embedding the naive Bayesian induction scheme within an algorithm that c arries out a greedy search through the space of features. We hypothesize that this approach will improve asymptotic accuracy in domains that involve correlated features without reducing the rate of learning in ones that do not. We report experimental results on six natural domains, including comparisons with decision-tree induction, that support these hypotheses. In closing, we discuss other approaches to extending naive Bayesian classifiers and outline some directions for future research.

IJCAI Conference 1983 Conference Paper

Modeling Cognitive Development on the Balance Scale Task

• Stephanie Sage
• Pat Langley

In this paper we describe a production system model of children's development on the balance scale task. Starting with a set of rules that makes random predictions, the system iearns from its errors and improves as it gains experience. The transition mechanism is a discrimination process that searches for differences between cases in which correct predictions are made and cases in which errors are made The stages through which the system progresses are very similar to those observed in children, so the model provides an explanation of the observed developmental trends Since the system has no notion of torque, it never acquires the ability to completely predict the balance scale's behavior; however, it is able to learn heuristically useful rules despite its incomplete representation of the environment, much as children do.