Abstract
This paper proposes an efficient method to learn from multi source data with an Inductive Logic Programming method. The method is based on two steps. The first one consists in learning rules independently from each source. In the second step the learned rules are used to bias a new learning process from the aggregated data. We validate this method on cardiac data obtained from electrocardiograms or arterial blood pressure measures. Our method is compared to a single step learning on aggregated data.
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© 2004 Springer-Verlag Berlin Heidelberg
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Fromont, É., Cordier, MO., Quiniou, R. (2004). Learning from Multi-source Data. In: Boulicaut, JF., Esposito, F., Giannotti, F., Pedreschi, D. (eds) Knowledge Discovery in Databases: PKDD 2004. PKDD 2004. Lecture Notes in Computer Science(), vol 3202. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30116-5_47
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DOI: https://doi.org/10.1007/978-3-540-30116-5_47
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-23108-0
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