Further Reading

Breiman L (2001) Random forests. Machine Learning 45: 5-32.

Breiman L, Friedman RA, Olshen RA, and Stone CG (1984)

Classification and Regression Trees. Pacific Grove, CA: Wadsworth.

Clark LA and Pregibon D (1992) Tree-based models. In: Chambers JM and Hastie TJ (eds.) Statistical Models in S, pp. 377-419. Pacific Grove, CA: Wadsworth and Brooks.

De'ath G and Fabricius KE (2000) Classification and regression trees: A powerful yet simple technique for ecological data analysis. Ecology 81: 3178-3192.

Everitt BS and Hothorn T (2006) A Handbook of Statistical Analyses using R. Boca Raton, FL: Chapman and Hall/CRC.

Friedman JH (2002) Stochastic gradient boosting. Computational Statistics and Data Analysis 38(4): 367-378.

Hastie T, Tibshirani R, and Friedman J (2001) The Elements of Statistical

Learning. New York: Springer. Murthy SK (1998) Automatic construction of decision trees from data: A multi-disciplinary survey. Data Mining and Knowledge Discovery 2: 345-389.

Quinlan JR (1993) C4.5: Programs for Machine Learning. San Mateo,

CA: Morgan Kaufmann. Ripley BD (1996) Pattern Recognition and Neural Networks. Cambridge: Cambridge University Press.

Steinberg D and Colla P (1995) CART: Tree-Structured Nonparametric

Data Analysis. San Diego, CA: Salford Systems. Vayssieres MP, Plant RP, and Allen-Diaz BH (2000) Classification trees: An alternative non-parametric approach for predicting species distributions. Journal of Vegetation Science 11: 679-694. Venables WN and Ripley BD (1999) Modern Applied Statistics with S-Plus. New York: Springer.

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