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- W2912709207 abstract "Machine learning's focus on ill-defined problems and highly flexible methods makes it ideally suited for knowledge discovery in databases (KDD) applications. Among the ideas machine learning contributes to KDD are the importance of empirical validation, the impossibility of learning without a priori assumptions, and the utility of limited-search or limited-representation methods. Machine learning provides methods for incorporating knowledge into the learning process, changing and combining representations, combatting the curse of dimensionality, and learning comprehensible models. KDD challenges for machine learning include scaling up its algorithms to large databases, using cost information in learning, automating data preprocessing, and enabling rapid development of applications. KDD opens up new directions for machine-learning research and brings new urgency to others. These directions include interfacing with the human user and the database system, learning from nonattribute-vector data, learning partial models, and learning continuously from an open-ended stream of data." @default.
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- W2912709207 date "2002-01-01" @default.
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- W2912709207 title "Machine learning" @default.
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