Random forests

Machine Learning

Volumn 45, Issue 1, 2001, Pages 5-32

  Breiman, Leo ^a  

^a UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

Classification; Ensemble; Regression

Indexed keywords

COMPUTATIONAL METHODS; COMPUTER SIMULATION; INFORMATION RETRIEVAL; RANDOM PROCESSES; REGRESSION ANALYSIS; SET THEORY; VECTORS;

RANDOM FORESTS;

TREES (MATHEMATICS);

EID: 0035478854     PISSN: 08856125     EISSN: None     Source Type: Journal    
DOI: 10.1023/A:1010933404324     Document Type: Article

References (17)

1. Shape quantization and recognition with randomized trees
2. An empirical comparison of voting classification algorithms
3. Bagging predictors
4. Arcing classifiers (discussion paper)
5. An experimental comparison of three methods for constructing ensembles of decision trees: Bagging, boosting and randomization
6. Experiments with a new boosting algorithm
7. Boosting in the limit: Maximizing the margin of learned ensembles
8. The random subspace method for constructing decision forests
9. On the algorithmic implementation of stochastic discrimination
10. Boosting the margin: A new explanation for the effectiveness of voting methods
11. An efficient method to estimate Bagging's generalization error