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Layered Learning for Concept Synthesis

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Transactions on Rough Sets I

Part of the book series: Lecture Notes in Computer Science ((TRS,volume 3100))

Abstract

We present a hierarchical scheme for synthesis of concept approximations based on given data and domain knowledge. We also propose a solution, founded on rough set theory, to the problem of constructing the approximation of higher level concepts by composing the approximation of lower level concepts. We examine the effectiveness of the layered learning approach by comparing it with the standard learning approach. Experiments are carried out on artificial data sets generated by a road traffic simulator.

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© 2004 Springer-Verlag Berlin Heidelberg

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Nguyen, S.H., Bazan, J., Skowron, A., Nguyen, H.S. (2004). Layered Learning for Concept Synthesis. In: Peters, J.F., Skowron, A., Grzymała-Busse, J.W., Kostek, B., Świniarski, R.W., Szczuka, M.S. (eds) Transactions on Rough Sets I. Lecture Notes in Computer Science, vol 3100. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-27794-1_9

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  • DOI: https://doi.org/10.1007/978-3-540-27794-1_9

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-22374-0

  • Online ISBN: 978-3-540-27794-1

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