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In this survey, we review work in machine learning on methods for handling data sets containing large amounts of irrelevant information. We focus on two key issues: the problem of selecting relevant features, and the problem of selecting relevant examples. We describe the advances that have been made on these topics in both empirical and theoretical work in(More)
In this paper, we examine previous work on the naive Bayesian classifier and review its limitations, which in­ clude a sensitivity to correlated features. We respond to this problem by embedding the naive Bayesian in­ duction scheme within an algorithm that carries out a greedy search through the space of features. We hy­ pothesize that this approach will(More)
6&. NAME OF PERFORMING ORGANIZATION 6b. OFFICE SYMBOL 7a. NAME OF MONITORING ORGANIZATION University of California (If applicable) U.S. Army Research Institute for at Irvine the Behavioral and Social Sciences 6c. ADDRESS (City, State, and ZIP Code) 7b. ADDRESS (City, State, and ZIP Code) Department of Information and Computer 5001 Eisenhower Avenue Science,(More)
1. I n t r o d u c t i o n In recent years, there has been growing interest in probabilistic methods for induction. Although much of the recent work in this area [e.g., 6] has focused on unsupervised learning, the approach applies equally well to supervised tasks. Such methods have long been used within the field of pattern recognition [4], but they have(More)
As data warehouses grow to the point where one hundred gigabytes is considered small, the computational efficiency of data-mining algorithms on large databases becomes increasingly important. Using a sample from the database can speed up the datamining process, but this is only acceptable if it does not reduce the quality of the mined knowledge. To this(More)
In this paper, we examine the motivations for research on cognitive architectures and review some candidates that have been explored in the literature. After this, we consider the capabilities that a cognitive architecture should support, some properties that it should exhibit related to representation, organization, performance, and learning, and some(More)
Increased computing power and the Web have made information widely accessible. In turn, this has encouraged the development of recommendation systems that help users find items of interest, such as books or restaurants. Such systems are more useful when they personalize themselves to each user’s preferences, thus making the recommendation process more(More)