Clifford Brunk

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We explore algorithms for learning classification procedures that attempt to minimize the cost of misclassifying examples. First, we consider inductive learning of classification rules. The Reduced Cost Ordering algorithm, a new method for creating a decision list (i.e., an ordered set of rules) is described and compared to a variety of inductive learning(More)
We describe an experimental study of pruning methods for decision tree classifiers in two learning situations: minimizing loss and probability estimation. In addition to the two most common methods for error minimization, CART'S cost-complexity pruning and C4.5'~ errorbased pruning, we study the extension of cost-complexity pruning to loss and two pruning(More)
In this paper, we argue that techniques proposed for combining empirical and explanation-based learning methods can also be used to detect errors in rule-based expert systems, to isolate the blame for these errors to a small number of rules and suggest revisions to the rules to eliminate these errors. We demonstrate that FOCL, an extension to Quinlan’s FOIL(More)
An approach to analytic learning is described that searches for accurate entailments of a Horn Clause domain theory. A hill-climbing search, guided by an information based evaluation function, is performed by applying a set of operators that derive frontiers from domain theories. The analytic learning system is one component of a multi-strategy relational(More)
In sparse data environments, greater classi cation accuracy can be achieved by learning several concept descriptions of the data and combining their classi cations. Stochastic search is a general tool which can be used to generate many good concept descriptions (rule sets) for each class in the data. Bayesian probability theory o ers an optimal strategy for(More)