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Improving Inductive learning in real-world domains through the identification of dependencies: The TIM Framework

  • 3 Machine Learning
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Tasks and Methods in Applied Artificial Intelligence (IEA/AIE 1998)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1416))

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Abstract

In this paper we describe TIM (Total Induction Method), a framework that empowers inductive learning in real domains by the construction of new higher level features based on the relations between the descriptors of the initial training set. A new method, named FDD, for discovering functional dependencies within the data is outlined, and details regarding its relevance for constructive learning are provided. Two examples of their application in real - world domains are given.

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Author information

Authors and Affiliations

  1. Facultad de Informática, Universidad Politécnica de Madrid, Campus de Montegancedo, 28660, Boadilla del Monte, Spain

    Juan P. Caraça-Valente & César Montes

Authors
  1. Juan P. Caraça-Valente
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  2. César Montes
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Editor information

Angel Pasqual del Pobil José Mira Moonis Ali

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© 1998 Springer-Verlag Berlin Heidelberg

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Caraça-Valente, J.P., Montes, C. (1998). Improving Inductive learning in real-world domains through the identification of dependencies: The TIM Framework. In: Pasqual del Pobil, A., Mira, J., Ali, M. (eds) Tasks and Methods in Applied Artificial Intelligence. IEA/AIE 1998. Lecture Notes in Computer Science, vol 1416. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-64574-8_431

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  • DOI: https://doi.org/10.1007/3-540-64574-8_431

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-64574-0

  • Online ISBN: 978-3-540-69350-5

  • eBook Packages: Springer Book Archive

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