Neural Random Forests

• Abadi, M., Agarwal, A., Barham, P., Brevdo, E., et al. (2015). TensorFlow: Large-scale machine learning on heterogeneous systems. http://tensorflow.org/.

• Bengio, Y. (2009). Learning deep architectures for AI. Foundations and Trends in Machine Learning2, 1–127.

  Article  Google Scholar 

• Biau, G. and Scornet, E. (2016). A random forest guided tour (with comments and a rejoinder by the authors). TEST25, 197–227.

  Article  MathSciNet  Google Scholar 

• Boulesteix, A.-L., Janitza, S., Kruppa, J. and König, I.R. (2012). Overview of random forest methodology and practical guidance with emphasis on computational biology and bioinformatics. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery2, 493–507.

  Google Scholar 

• Breiman, L. (2001). Random forests. Machine Learning45, 5–32.

  Article  Google Scholar 

• Breiman, L., Friedman, J.H., Olshen, R.A. and Stone, C.J. (1984). Classification and regression trees. Chapman & Hall/CRC, Boca Raton.

  MATH  Google Scholar 

• Brent, R.P. (1991). Fast training algorithms for multi-layer neural nets. IEEE Transactions on Neural Networks2, 346–354.

  Article  Google Scholar 

• Chipman, H.A., George, E.I. and McCulloch, R.E. (2010). BART: Bayesian additive regression trees. The Annals of Applied Statistics4, 266–298.

  Article  MathSciNet  Google Scholar 

• Cortez, P. and Morais, A. (2007). A data mining approach to predict forest fires using meteorological data. In: J. Neves, M.F. Santos, and J. Machado, editors, New Trends in Artificial Intelligence, Proceedings of the 13th Portugese Conference on Artificial Intelligence, pp. 512–523.

• Devroye, L., Györfi, L. and Lugosi, G. (1996). A Probabilistic Theory of Pattern Recognition. Springer, New York.

  Book  Google Scholar 

• Fernández-Delgado, M., Cernadas, E., Barro, S. and Amorim, D. (2014). Do we need hundreds of classifiers to solve real world classification problems? Journal of Machine Learning Research15, 3133–3181.

  MathSciNet  MATH  Google Scholar 

• Geurts, P. and Wehenkel, L. (2005). Closed-form dual perturb and combine for tree-based models. ACM, New York, pp. 233–240.

• Györfi, L., Kohler, M., KrzyŻak, A. and Walk, H. (2002). A Distribution-Free Theory of Nonparametric Regression. Springer, New York.

  Book  Google Scholar 

• Hastie, T., Tibshirani, R. and Friedman, J. (2009). The Elements of Statistical Learning, 2nd edition. Springer, New York.

  Book  Google Scholar 

• Ioannou, Y., Robertson, D., Zikic, D., Kontschieder, P., Shotton, J., Brown, M. and Criminisi, A. (2016). Decision forests, convolutional networks and the models in-between. arXiv:1603.01250.

• Ishwaran, H., Kogalur, U.B., Chen, X. and Minn, A.J. (2011). Random survival forests for high-dimensional data. Statistical Analysis and Data Mining4, 115–132.

  Article  MathSciNet  Google Scholar 

• Jordan, M.I. and Jacobs, R.A. (1994). Hierarchical mixtures of experts and the EM algorithm. Neural Computation6, 181–214.

  Article  Google Scholar 

• Kingma, D.P. and Ba, J. (2015). Adam: A method for stochastic optimization. In: International conference on learning representations.

• Kontschieder, P., Fiterau, M., Criminisi, A. and Rota Bulò, S. (2015). Deep neural decision forests. In: International conference on computer vision.

  Book  Google Scholar 

• Lichman, M. (2013). UCI machine learning repository. http://archive.ics.uci.edu/ml.

  Google Scholar 

• Lugosi, G. and Zeger, K. (1995). Nonparametric estimation via empirical risk minimization. IEEE Transactions on Information Theory41, 677–687.

  Article  MathSciNet  Google Scholar 

• Meinshausen, N. (2006). Quantile regression forests. Journal of Machine Learning Research7, 983–999.

  MathSciNet  MATH  Google Scholar 

• Menze, B.H., Kelm, B.M., Splitthoff, D.N., Koethe, U. and Hamprecht, F.A. (2011). On oblique random forests. Machine Learning and Knowledge Discovery in Databases, Springer, Berlin, D. Gunopulos, T. Hofmann, D. Malerba, and M. Vazirgiannis, editors, pages 453–469.

• Olaru, C. and Wehenkel, L. (2003). A complete fuzzy decision tree technique. Fuzzy Sets and Systems138, 221–254.

  Article  MathSciNet  Google Scholar 

• Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., et al. (2011). Scikit-learn: Machine learning in Python. Journal of Machine Learning Research12, 2825– 2830.

  MathSciNet  MATH  Google Scholar 

• Pollard, D. (1984). Convergence of Stochastic Processes. Springer, New York.

  Book  Google Scholar 

• Redmond, M. and Baveja, A. (2002). A data-driven software tool for enabling cooperative information sharing among police departments. European Journal of Operational Research141, 660–678.

  Article  Google Scholar 

• Richmond, D.L., Kainmueller, D., Yang, M.Y., Myers, E.W. and Rother, C. (2015). Relating cascaded random forests to deep convolutional neural networks for semantic segmentation. arXiv:1507:07583.

• Rokach, L. and Maimon, O. (2008). Data Mining with Decision Trees: Theory and Applications. World Scientific, Singapore.

  MATH  Google Scholar 

• Scornet, E., Biau, G. and Vert, J.-P. (2015). Consistency of random forests. The Annals of Statistics43, 1716–1741.

  Article  MathSciNet  Google Scholar 

• Sethi, I.K. (1990). Entropy nets: From decision trees to neural networks. Proceedings of the IEEE78, 1605–1613.

  Article  Google Scholar 

• Sethi, I.K. (1991). Decision tree performance enhancement using an artificial neural network interpretation. Elsevier, Amsterdam, Sethi, I.K. and Jain, A.K. (eds.), 6912, pp. 71–88.

• Welbl, J. (2014). Casting random forests as artificial neural networks (and profiting from it). Pattern Recognition, Springer, Berlin Jiang, X., Hornegger, J., Koch, R. (eds.), pp. 765–771.

• Yeh, I.-C. (1998). Modeling of strength of high-performance concrete using artificial neural networks. Cement and Concrete Research28, 1797–1808.

  Article  Google Scholar 

• Yildiz, O.T. and Alpaydin, E. (2013). Regularizing soft decision trees. Information Sciences and Systems 2013, Springer, Cham,. pp. 15–21.

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