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Abstract: . When learning from very large databases, the reduction of complexity is extremely important. Two extremes of making knowledge discovery in databases (KDD) feasible have been put forward. One extreme is to choose a very simple hypothesis language, thereby being capable of very fast learning on real-world databases. The opposite extreme is to select a small data set, thereby being able to learn very expressive (first-order logic) hypotheses. A multistrategy approach allows one to include most... (Update)
Context of citations to this paper: More
...evidence in favour or against the hypothesis mentioned above. For all learning experiments we used the learning system Rdt Db (Morik and Brockhausen, 1997), which is a variation of Rdt (Kietz and Wrobel, 1992) Rdt Db uses for hypothesis generation the core of Rdt, but for...
...which are able to handle several relations at once. Whereas some ILP algorithms are able to learn on databases directly [Morik and Brockhausen, 1997], others are unable to make use of a relational database. Looking at new developments in ILP like CILGG, which provides a very...
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BibTeX entry: (Update)
Katharina Morik and Peter Brockhausen. A multistrategy approach to relational knowledge discovery in databases. In Ryszard S. Michalski and Janusz Wnek, editors, Proceedings of the Third International Workshop on Multistrategy Learning (MSL-96), Palo Alto, May 1996. AAAI Press. http://citeseer.ist.psu.edu/article/morik96multistrategy.html More
@inproceedings{ morik96multistrategy,
author = "K. Morik and P. Brockhausen",
title = "A Multistrategy Approach to Relational Knowledge Discovery in Databases",
booktitle = "Proceedings of the 3nd International Workshop on Multistrategy Learning",
publisher = "AAAI Press",
pages = "17--28",
year = "1996",
url = "citeseer.ist.psu.edu/article/morik96multistrategy.html" }
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