Abstract
This paper addresses the problem of learning from highly structured data. Specifically, it describes a procedure, called decomposition, that allows a learner to access automatically the subparts of examples represented as closed terms in a higher-order language. This procedure maintains a clear distinction between the structure of an individual and its properties. A learning system based on decomposition is also presented and several examples of its use are described.
This work is funded in part by grants from CONACYT and UAM, México
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© 1999 Springer-Verlag Berlin Heidelberg
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Kinney-Romero, R.M., Giraud-Carrier, C. (1999). Learning from Highly Structured Data by Decomposition. In: Żytkow, J.M., Rauch, J. (eds) Principles of Data Mining and Knowledge Discovery. PKDD 1999. Lecture Notes in Computer Science(), vol 1704. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-48247-5_55
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DOI: https://doi.org/10.1007/978-3-540-48247-5_55
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-66490-1
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