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We show that the familiar explanation-based generalization (EBG) procedure is applicable to a large family of programming languages, including three families of importance to AI: logic programming (such as Pro-log); lambda calculus (such as LISP); and combinator languages (such as FP). The main application of this result is to extend the algorithm to(More)
– The current approaches for linking information across sources, often called record linkage, require finding common attributes between the sources and comparing the records using those attributes. This often leads to unsatisfactory results because the sources are often missing information or contain incorrect or outdated information. We are addressing this(More)
Real-world data is virtually never noise-free. Current methods for handling noise do so either by removing noisy instances or by trying to clean noisy attributes. Neither of these deal directly with the issue of noise and in fact removing a noisy instance is not a viable option in many real systems. In this paper, we consider the problem of noise in the(More)
Kohonen and others have devised network algorithms for computing so-calledtopological feature maps. We describe a new algorithm, called theCDF-Inversion (CDFI) Algorithm, that can be used to learn feature maps and, in the process, approximate an unknown probalility distribution to within any specified accuracy. The primary advantags of the algorithm over(More)
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