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2000, ISBN 9789067643146, 122
Book
Pattern Recognition Letters, ISSN 0167-8655, 2010, Volume 31, Issue 14, pp. 2225 - 2236
This paper proposes, focusing on random forests, the increasingly used statistical method for classification and regression problems introduced by Leo Breiman... 
Regression | High dimensional data | Variable importance | Random forests | Classification | Variable selection | LINEAR-REGRESSION | GENE SELECTION | CANCER | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | Usage | Analysis | Forests and forestry | Statistics | Statistics Theory | Mathematics
Journal Article
IEEE Transactions on Pattern Analysis and Machine Intelligence, ISSN 0162-8828, 08/2015, Volume 37, Issue 8, pp. 1558 - 1570
Journal Article
The Annals of Statistics, ISSN 0090-5364, 8/2015, Volume 43, Issue 4, pp. 1716 - 1741
Journal Article
Journal Article
Journal of Machine Learning Research, ISSN 1533-7928, 06/2006, Volume 7, pp. 983 - 999
Random forests were introduced as a machine learning tool in Breiman (2001) and have since proven to be very popular and powerful for high-dimensional... 
Quantile regression | Adaptive neighborhood regression | Random forests | quantile regression | random forests | adaptive neighborhood regression | TREES | AUTOMATION & CONTROL SYSTEMS | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Journal Article
Journal of Classification, ISSN 0176-4268, 2019, pp. 1 - 14
The ordinal forest method is a random forest–based prediction method for ordinal response variables. Ordinal forests allow prediction using both... 
Covariate importance ranking | Random forest | Prediction | Ordinal response variable | Comparative analysis | Algorithms | Predictions
Journal Article
Journal Article
Journal Article
Journal of Machine Learning Research, ISSN 1532-4435, 04/2012, Volume 13, pp. 1063 - 1095
Random forests are a scheme proposed by Leo Breiman in the 2000's for building a predictor ensemble with a set of decision trees that grow in randomly selected... 
Dimension reduction | Randomization | Sparsity | Rate of convergence | Random forests | Consistency | REGRESSION | DANTZIG SELECTOR | rate of convergence | random forests | CLASSIFICATION | sparsity | dimension reduction | consistency | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | TREES | randomization | LASSO | NEAREST-NEIGHBOR ESTIMATE | AUTOMATION & CONTROL SYSTEMS | Probability | Mathematics
Journal Article
Remote Sensing of Environment, ISSN 0034-4257, 2011, Volume 115, Issue 10, pp. 2564 - 2577
Journal Article
Pattern Recognition Letters, ISSN 0167-8655, 09/2012, Volume 33, Issue 12, pp. 1580 - 1586
► A new principle of Random Forest (RF) induction called Dynamic Random Forests (DRF) is presented. ► A first variant of this learning principle is proposed... 
Ensemble of classifiers | Dynamic induction | Random feature selection | Random forests | RECOGNITION | PREDICTORS | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | Forests and forestry | Algorithms | Document and Text Processing | Computer Science
Journal Article
Journal Article
Big Data Research, ISSN 2214-5796, 09/2017, Volume 9, pp. 28 - 46
Big Data is one of the major challenges of statistical science and has numerous consequences from algorithmic and theoretical viewpoints. Big Data always... 
Big Data | Parallel computing | Random forest | Bag of little bootstraps | On-line learning | MINING DATA | CONSISTENCY | BOOTSTRAP | COMPUTER SCIENCE, INFORMATION SYSTEMS | COMPUTER SCIENCE, THEORY & METHODS | CHALLENGES | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | Statistics | Machine Learning | Mathematics
Journal Article