A general framework for accurate and fast regression by data summarization in random decision trees
- 20 August 2006
- conference paper
- conference paper
- Published by Association for Computing Machinery (ACM)
Abstract
No abstract availableThis publication has 12 references indexed in Scilit:
- Learning through Changes: An Empirical Study of Dynamic Behaviors of Probability Estimation TreesPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2006
- Effective Estimation of Posterior Probabilities: Explaining the Accuracy of Randomized Decision Tree ApproachesPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2006
- COMBINING REGRESSION ESTIMATORS: GA-BASED SELECTIVE NEURAL NETWORK ENSEMBLEInternational Journal of Computational Intelligence and Applications, 2001
- Goodness-of-Fit Tests for Parametric Regression ModelsJournal of the American Statistical Association, 2001
- Random ForestsMachine Learning, 2001
- Shape Quantization and Recognition with Randomized TreesNeural Computation, 1997
- Applied Nonparametric RegressionPublished by Cambridge University Press (CUP) ,1990
- Generalized Linear ModelsPublished by Springer Science and Business Media LLC ,1989
- Generalized Additive ModelsStatistical Science, 1986
- Generalized Linear ModelsJournal of the Royal Statistical Society. Series A (General), 1972