Titlebook: Machine Learning and Its Applications; Advanced Lectures Georgios Paliouras,Vangelis Karkaletsis,Constantin Textbook 2001 Springer-Verlag B

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Machine Learning in User Modelings well as the development of “personalized” retrieval and filtering mechanisms. Responses to this challenge come from various disciplines including machine learning and data mining, intelligent agents and multi-agent systems, intelligent tutoring, information retrieval, etc.

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The Computational Support of Scientic Discoverydly questioned this attitude and attempted to develop intelligent artifacts that replicate the act of discovery. The computational study of scientific discovery has made important strides in its short history, some of which we review in this paper.

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Comparing Machine Learning and Knowledge Discovery in DataBases: An Application to Knowledge Discove) insisting on how much they actually differ. In order to make my ideas somewhat easier to understand, and as an illustration, I will include a description of several research topics that I find relevant to KDD and to KDD only. The second goal is to show that the definition I give of KDD can be almo

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Unsupervised Learning of Probabilistic Concept Hierarchieshing, this trend has been strengthened by the creation of data repositories that, typically, include class information. But this emphasis is misguided if we want to understand the nature of learning in intelligent agents like humans. Clearly, children acquire many concepts about the world before the

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