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Hyper-rectangular space partitioning trees: A practical approach

Bibliographic reference De Macq, Isabelle ; Simar, Léopold. Hyper-rectangular space partitioning trees: A practical approach. In: Computational Statistics, Vol. 20, no. 1, p. 119-135 (2005)
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  1. Breiman, L., Friedman, J., Olshen, R. & Stone, C. (1984),Classification and Regression Trees., Wadsworth International, Belmont, CA.
  2. Buntine, W. & Caruana, R. (1992),Introduction to IND Version 2.1 and Recursive Partitioning, NASA Ames Research Center, Moffet Field, CA.
  3. Clark, L. & Pregibon, D. (1993), Tree-based models,in J. Chambers & T. Hastie, eds,‘Statistical Models in S’ Chapman & Hall, New York, NY, pp. 377–419.
  4. Devroye Luc, Györfi László, Lugosi Gábor, A Probabilistic Theory of Pattern Recognition, ISBN:9781461268772, 10.1007/978-1-4612-0711-5
  5. Esposito, F., Malerba, D. & Semeraro, G. (1997),‘A comparative analysis of methods for pruning decision trees.’IEEE Transactions on pattern analysis and machine intelligence 19(5), 476–491.
  6. Friedman, J. & Fisher, N. (1999),‘Bump hunting in high-dimensional data’Statistics and Computing 9(2), 123–143.
  7. Michie, D., Spiegelhalter, D. J. & Taylor, C. C. (1994),Machine Learning, neural and statistical classification., Ellis Horwood.
  8. Muller, W. & Wysotzki, F. (1997), The decision tree algorithm CAL5 based on a statistical approach to its splitting algorithm,in‘Machine Learning and Statistics: The Interface’ John Wiley & Sons, New York, NY, pp. 45–65.
  9. Murphy, P. M. & Aha, D. W. (1996),UCI Repository of machine learning databases., Department of Information and Computer Science, University of California, Irvine, CA.
  10. Quinlan, J. R. (1993),C4.5: Programs for Machine Learning, San Mateo, CA: Morgan Kaufmann.
  11. Shih, Y.-S., Lim, T.-S. & Loh, W.-Y. (2000),‘A comparison of prediction accuracy, complexity and training time of thirty-three old and new classification algorithms’Machine Learning 40, 203–228.
  12. Venables W. N., Ripley B. D., Modern Applied Statistics with S-Plus, ISBN:9781489928214, 10.1007/978-1-4899-2819-1