Abstract
In this paper we describe the non-covering inductive logic programming program HYPER/N, concentrating mainly on noise handling as well as some other mechanisms that improve learning. We perform some experiments with HYPER/N on synthetic weather data with artificially added noise, and on real weather data to learn to predict the movement of rain from radar rain images and synoptic data.
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© 2011 Springer-Verlag Berlin Heidelberg
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Oblak, A., Bratko, I. (2011). Learning from Noisy Data Using a Non-covering ILP Algorithm. In: Frasconi, P., Lisi, F.A. (eds) Inductive Logic Programming. ILP 2010. Lecture Notes in Computer Science(), vol 6489. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21295-6_22
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DOI: https://doi.org/10.1007/978-3-642-21295-6_22
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-21294-9
Online ISBN: 978-3-642-21295-6
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