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Conventional constraint systems are suitable for finding assignments to a finite set of parameters such that a set of constraints are satisfied. However, in generalized problem-solving, the set of parameters for which values must be found is only a subset of a much larger (possibly infinite) set of parameters, and the membership of this subset is dependent on conditions whose truth or falsity can...
Constraint satisfaction problems (CSPs) are ubiquitous in artificial intelligence; versions arise in areas such as vision, design, Boolean satisfiability, cryptarithmetic, and database retrieval. Most researchers have solved CSPs on sequential computers; relatively few have addressed the use of parallel computers for these problems. Among those who have investigated parallel approaches, several authors...
Learning with imbalanced datasets has been a major topic of study for many years. In this paper, we focus on a type of imbalance called imbalance due to rare instances. Such imbalances occur in a variety of domains. Rare instances have received less focus in prediction problems and we wish to draw attention to how accuracy can be improved in the presence of rare data. We discuss an approach to regression...
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