A working guide to boosted regression trees.
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J Elith | J R Leathwick | T Hastie | T. Hastie | J. Elith | J. Leathwick
[1] G. Cumming,et al. Editors Can Lead Researchers to Confidence Intervals, but Can't Make Them Think , 2004, Psychological science.
[2] David R. Anderson,et al. Model selection and multimodel inference : a practical information-theoretic approach , 2003 .
[3] Robert E. Schapire,et al. The Boosting Approach to Machine Learning An Overview , 2003 .
[4] R. McDowall,et al. Implications of diadromy for the structuring and modelling of riverine fish communities in New Zealand , 1993 .
[5] W. L. Chadderton,et al. Dispersal, disturbance and the contrasting biogeographies of New Zealand’s diadromous and non‐diadromous fish species , 2008 .
[6] P. McCullagh,et al. Generalized Linear Models, 2nd Edn. , 1990 .
[7] A. Townsend Peterson,et al. Novel methods improve prediction of species' distributions from occurrence data , 2006 .
[8] Jerome H Friedman,et al. Multiple additive regression trees with application in epidemiology , 2003, Statistics in medicine.
[9] B. Reineking,et al. Constrain to perform: Regularization of habitat models , 2006 .
[10] Alan J. Miller. Subset Selection in Regression , 1992 .
[11] P. McCullagh,et al. Generalized Linear Models , 1992 .
[12] Alan J. Miller,et al. Subset Selection in Regression , 1991 .
[13] Glenn De ' ath. BOOSTED TREES FOR ECOLOGICAL MODELING AND PREDICTION , 2007 .
[14] Robert Tibshirani,et al. The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd Edition , 2001, Springer Series in Statistics.
[15] David A. Elston,et al. Empirical models for the spatial distribution of wildlife , 1993 .
[16] A. Clarke,et al. Scaling of metabolic rate with body mass and temperature in teleost fish , 1999 .
[17] T. Hastie,et al. Variation in demersal fish species richness in the oceans surrounding New Zealand: an analysis using boosted regression trees , 2006 .
[18] Leo Breiman,et al. Statistical Modeling: The Two Cultures (with comments and a rejoinder by the author) , 2001, Statistical Science.
[19] J. Friedman. Stochastic gradient boosting , 2002 .
[20] Leo Breiman,et al. Statistical Modeling: The Two Cultures (with comments and a rejoinder by the author) , 2001 .
[21] Yoav Freund,et al. Experiments with a New Boosting Algorithm , 1996, ICML.
[22] Mark R. Segal,et al. Machine Learning Benchmarks and Random Forest Regression , 2004 .
[23] Y. Freund,et al. Discussion of the Paper \additive Logistic Regression: a Statistical View of Boosting" By , 2000 .
[24] David R. Anderson,et al. Model Selection and Inference: A Practical Information-Theoretic Approach , 2001 .
[25] J. Friedman. Greedy function approximation: A gradient boosting machine. , 2001 .
[26] R. Tibshirani,et al. Generalized Additive Models , 1991 .
[27] G. De’ath,et al. CLASSIFICATION AND REGRESSION TREES: A POWERFUL YET SIMPLE TECHNIQUE FOR ECOLOGICAL DATA ANALYSIS , 2000 .
[28] Robert P Freckleton,et al. Why do we still use stepwise modelling in ecology and behaviour? , 2006, The Journal of animal ecology.
[29] S. T. Buckland,et al. ANALYSIS OF POPULATION TRENDS FOR FARMLAND BIRDS USING GENERALIZED ADDITIVE MODELS , 2000 .
[30] Ron Kohavi,et al. A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection , 1995, IJCAI.
[31] Niklaus E. Zimmermann,et al. Predicting tree species presence and basal area in Utah: A comparison of stochastic gradient boosting, generalized additive models, and tree-based methods , 2006 .
[32] Leo Breiman,et al. Classification and Regression Trees , 1984 .
[33] A. Prasad,et al. Newer Classification and Regression Tree Techniques: Bagging and Random Forests for Ecological Prediction , 2006, Ecosystems.