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ABSTRACT
Machine learning is the study of computational methods for improving performance by mechanizing the acquisition of knowledge from experience. Expert performance requires much domain-specific knowledge, and knowledge engineering has produced hundreds of AI expert systems that are now used regularly in industry. Machine learning aims to provide increasing levels of automation in the knowledge engineering process, replacing much time-consuming human activity with automatic techniques that improve accuracy or efficiency by discovering and exploiting regularities in training data. The ultimate test of machine learning is its ability to produce systems that are used regularly in industry, education, and elsewhere.
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Nada Lavrač , Hiroshi Motoda , Tom Fawcett , Robert Holte , Pat Langley , Pieter Adriaans, Introduction: Lessons Learned from Data Mining Applications and Collaborative Problem Solving, Machine Learning, v.57 n.1-2, p.13-34, October-November 2004
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Selim Aksoy , Krzysztof Koperski , Carsten Tusk , Giovanni Marchisio, Interactive training of advanced classifiers for mining remote sensing image archives, Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining, August 22-25, 2004, Seattle, WA, USA
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Michael C. Burl , Lars Asker , Padhraic Smyth , Usama Fayyad , Pietro Perona , Larry Crumpler , Jayne Aubele, Learning to Recognize Volcanoes on Venus, Machine Learning, v.30 n.2-3, p.165-194, Feb./ March, 1998
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Athanasios Tsakonas , Georgios Dounias , Jan Jantzen , Hubertus Axer , Beth Bjerregaard , Diedrich Graf von Keyserlingk, Evolving rule-based systems in two medical domains using genetic programming, Arificial Intelligence in Medicine, v.32 n.3, p.195-216, November 2004
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REVIEW
"Daniel L. Chester : Reviewer"
Rule induction, one of the five basic paradigms in
machine learning, is covered most interestingly in this paper. (The
other four paradigms are neural networks, case-based learning, genetic
algorithms, and analytic learning.) Most
more...
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