God doesn't always shave with Occam's razor — Learning when and how to prune

Author:

Bensusan Hilan

Publisher

Springer Berlin Heidelberg

Reference8 articles.

1. H. Bensusan and P. Williams. Learning to learn boolean tasks by decision tree descriptors. In M. V. Someren and G. Widmer, editors, Poster Papers — 9th European Conference on Machine Learning, pages 1–11. 1997.

2. P. Brazdil, J. Gama, and B. Henery. Characterizing the applicability of classification algorithms using Meta-Level Learning. In Francesco Bergadano and Luc de Raedt, editors, Proceedings of the European Conference on Machine Learning, volume 784 of LNAI, pages 83–102, Berlin, April 1994. Springer.

3. S. Thrun et al. The monk's problems. Technical Report CMU-CS-91-197, School of Computer Science, Carnegie-Mellon University., Pittsburgh, PA-USA, 1991.

4. D. Gamberger and N. Lavrac. Conditions for Occam's Razor applicability and noise elimination. In Marteen van Someren and Gerhard Widmer, editors, Proceedings of the 9th European Conference on Machine Learning, pages 108–123. Springer, 1997.

5. J. Mingers. An empirical comparison of pruning methods for decision tree induction. Machine Learning, 4:227–243, 1989.

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