Looking for lumps: Boosting and bagging for density estimation

Computational Statistics and Data Analysis

Volumn 38, Issue 4, 2002, Pages 379-392

  Ridgeway, Greg ^a  

^a RAND CORPORATION   (United States)

Author keywords

Bagging; Boosting; Data mining; Density estimation

Indexed keywords

ALGORITHMS; COMPUTER SIMULATION; DATA STRUCTURES; ITERATIVE METHODS; OPTIMIZATION;

DATASETS;

DATA MINING;

NUMERICAL METHOD;

EID: 0037186537     PISSN: 01679473     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0167-9473(01)00066-4     Document Type: Article

References (25)

1. Comparative accuracies of artificial neural networks and discriminant analysis in predicting forest cover types from carto-graphic variables
2. Bagging predictors
3. Prediction games and arcing algorithms
4. Using iterated bagging to debias regressions
5. Maximum likelihood from incomplete data via the EM algorithm
6. Squashing flat files flatter
7. A decision-theoretic generalization of on-line learning and an application to boosting
8. Bias, variance, 0/1 loss, and the curse of dimensionality
9. Greedy function approximation: A gradient boosting machine
10. Additive logistic regression: A statistical view of boosting (with discussion)
11. Acceleration of the EM algorithm by using quasi-Newton methods
12. Deterministic annealing for density estimation by multivariate normal mixtures
13. Mixture density estimation
14. Finding the observed information matrix when using the EM algorithm
15. Likelihood-based data squashing: A modeling approach to instance construction
16. Boosting algorithms as gradient descent
17. A fast improvement to the EM algorithm on its own terms
18. Measuring mass concentrations and estimating density contour clusters - An excess mass approach
19. The state of boosting