Abstract
Serious meta-learning applications may require running huge counts of learning processes. The results obtained from the calculations must be reliably analyzed by many comparisons and statistical tests. To gain valuable knowledge about data at hand, it does not suffice to run a couple of methods and show the results. Also in scientific experiments, it should no longer be accepted, that several algorithms are tried and new approaches are claimed to be advantageous, on the basis of several simple tests and comparisons to several other methods.
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References
Frank A, Asuncion A (2010) UCI machine learning repository. http://archive.ics.uci.edu/ml
Grąbczewski K, Jankowski N (2008) Meta-learning with machine generators and complexity controlled exploration. In: Artificial intelligence and soft computing. Lecture notes in computer science. Springer, Berlin, pp 545–555
Jankowski N, Grąbczewski K (2008) Building meta-learning algorithms basing on search controlled by machine’s complexity and machines generators. In: IEEE world congress on computational intelligence, IEEE Press, pp 3600–3607
Lowry R (1998–2013) Concepts and applications of inferential statistics. http://vassarstats.net/textbook/
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Grąbczewski, K. (2014). Intemi: Advanced Meta-Learning Framework. In: Meta-Learning in Decision Tree Induction. Studies in Computational Intelligence, vol 498. Springer, Cham. https://doi.org/10.1007/978-3-319-00960-5_4
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DOI: https://doi.org/10.1007/978-3-319-00960-5_4
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