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The ability to restructure a decision tree eeciently enables a variety of approaches to decision tree induction that would otherwise be prohibitively expensive. Two such approaches are described here, one being incremental tree induction (ITI), and the other being non-incremental tree induction using a measure of tree quality instead of test quality (DMTI).(More)
Learning methods vary in the optimism or pessimism with which they regard the informativeness of learned knowledge. Pessimism is implicit in hypothesis testing, where we wish to draw cautious conclusions from experimental evidence. However, this paper demonstrates that optimism in the utility of derived rules may be the preferred bias for learning systems(More)
We investigate the problem of keeping the plans of multiple agents synchronized during execution. We assume that agents only have a partial view of the overall plan. They know the tasks they must perform, and know the tasks of other agents with whom they have direct dependencies. Initially, agents are given a schedule of tasks to perform together with a(More)
We present a way of approximating the posterior probability of a rule-set model that is comprised of a set of class descriptions. Each class description, in turn, consists of a set of relational rules. The ability to compute this posterior and to learn many models from the same training set allows us to approximate the expectation that an example to be(More)
The future explanatory power in biomedicine will be at the molecular-genetic level of analysis (rather than the epidemiologic-demographic or anatomic-cellular levels). This is the level of complex systems. Complex systems are characterized by nonlinearity and complex interactions. It is diicult for traditional statistical methods to capture complex systems(More)
Naive Bayesian classiiers which make independence assumptions perform remarkably well on some data sets but poorly on others. We explore ways to improve the Bayesian classiier by searching for dependencies among attributes. We propose and evaluate two algorithms for detecting dependencies among attributes and show that the backward sequential elimination(More)