-
1
-
-
84857438591
-
-
eds., Springer- Verlag, Berlin
-
Banks, D., L. House, P. Arabie, F.R. McMorris, and W. Gaul, eds. 2004. Classification, Cluster Analysis, and Data Mining, Springer- Verlag, Berlin.
-
(2004)
Classification, Cluster Analysis, and Data Mining
-
-
Banks, D.1
House, L.2
Arabie, P.3
McMorris, F.R.4
Gaul, W.5
-
2
-
-
84857415609
-
-
Duke University, Aug. 29-Nov. 28
-
Banks, D. 2007. Lectures on Statistical Data Mining, Duke University, Aug. 29-Nov. 28. http://www.stat.duke.edu/~banks/218-lectures.dir/
-
(2007)
Lectures On Statistical Data Mining
-
-
Banks, D.1
-
3
-
-
0032645080
-
An Empirical Comparison of Voting Classification Algorithms
-
Bauer, E. and Kohavi, R. 1999. 'An Empirical Comparison of Voting Classification Algorithms,' Machine Learning, 36, No. 1/2, 105-139.
-
(1999)
Machine Learning
, vol.36
, Issue.1-2
, pp. 105-139
-
-
Bauer, E.1
Kohavi, R.2
-
5
-
-
31944452324
-
An Introduction to Ensemble Methods for Data Analysis
-
(February)
-
Berk, R. 2006. 'An Introduction to Ensemble Methods for Data Analysis.' Sociological Methods and Research, 34: 3, (February), 263-95.
-
(2006)
Sociological Methods and Research
, vol.34
, Issue.3
, pp. 263-295
-
-
Berk, R.1
-
6
-
-
26444580147
-
Statistical Difficulties in Determining the Role of Race in Capital Cases
-
Berk, R., A. Li and L. Hickman. 2005. 'Statistical Difficulties in Determining the Role of Race in Capital Cases', Journal of Quantitative Criminology, 21: 4, 365-390.
-
(2005)
Journal of Quantitative Criminology
, vol.21
, Issue.4
, pp. 365-390
-
-
Berk, R.1
Li, A.2
Hickman, L.3
-
9
-
-
0003802343
-
-
Monterey, CA: Wadsworth
-
Breiman, L., J.H. Friedman, R.A. Olshen, and C.J. Stone. 1984. Classification and Regression Trees. Monterey, CA: Wadsworth.
-
(1984)
Classification and Regression Trees
-
-
Breiman, L.1
Friedman, J.H.2
Olshen, R.A.3
Stone, C.J.4
-
10
-
-
0000343716
-
Submodel selection and evaluation in regression: The X-random case
-
Breiman, L., and P. Spector. 1992. 'Submodel selection and evaluation in regression: The X-random case,' International Statistical Review, 60: 291-319.
-
(1992)
International Statistical Review
, vol.60
, pp. 291-319
-
-
Breiman, L.1
Spector, P.2
-
11
-
-
0030211964
-
Bagging Predictors
-
Breiman, L. 1996a. 'Bagging Predictors.' Machine Learning 26: 123-40.
-
(1996)
Machine Learning
, vol.26
, pp. 123-140
-
-
Breiman, L.1
-
13
-
-
0006001358
-
-
UC Berkeley, Statistics Department, Technical Report N
-
Breiman, L. 1999. 'Random Forests-Random Features.' UC Berkeley, Statistics Department, Technical Report N. 567.
-
(1999)
Random Forests-Random Features
, pp. 567
-
-
Breiman, L.1
-
14
-
-
0035478854
-
Random Forests
-
Breiman, L. 2001a. 'Random Forests.' Machine Learning 45: 5-32.
-
(2001)
Machine Learning
, vol.45
, pp. 5-32
-
-
Breiman, L.1
-
15
-
-
0000245743
-
Statistical Modeling: Two Cultures (with discussion)
-
Breiman, L. 2001b. 'Statistical Modeling: Two Cultures' (with discussion). Statistical Science 16: 199-231.
-
(2001)
Statistical Science
, vol.16
, pp. 199-231
-
-
Breiman, L.1
-
17
-
-
33745182398
-
Consistency For A SimpleModel Of RandomForests
-
Statistics Department University Of California at Berkeley, September 9, 2004
-
Breiman, L. 2004a. 'Consistency For A SimpleModel Of RandomForests,' Technical Report 670, Statistics Department University Of California at Berkeley, September 9, 2004.
-
(2004)
Technical Report
, vol.670
-
-
Breiman, L.1
-
21
-
-
0036643067
-
Estimating Generalization Error on Two-Class Datasets Using Out-of-Bag Estimates
-
Bylander, T. 2002. 'Estimating Generalization Error on Two-Class Datasets Using Out-of-Bag Estimates,' Machine Learning 48, 1-3, p. 287-297.
-
(2002)
Machine Learning
, vol.48
, Issue.1-3
, pp. 287-297
-
-
Bylander, T.1
-
22
-
-
43949125818
-
Evaluation of Random Forest and Adaboost tree-based ensemble classification and spectral band selection for ecotope mapping using airborne hyperspectral imagery
-
16 June 2008
-
Chan, J.C-W. and D. Paelinckx. 2008. 'Evaluation of Random Forest and Adaboost tree-based ensemble classification and spectral band selection for ecotope mapping using airborne hyperspectral imagery,' Remote Sensing of Environment 112, 6, 16 June 2008, 2999-3011.
-
(2008)
Remote Sensing of Environment
, vol.112
, Issue.6
, pp. 2999-3011
-
-
Chan, J.C-W.1
Paelinckx, D.2
-
23
-
-
0000057576
-
Controlling bias in observational studies: A review
-
Series A
-
Cochran, W.G., and D.B. Rubin. 1973. Controlling bias in observational studies: A review. Sankhya: The Indian Journal of Statistics, Series A 35(Part 4): 417-66.
-
(1973)
Sankhya: The Indian Journal of Statistics
, vol.35
, Issue.PART. 4
, pp. 417-466
-
-
Cochran, W.G.1
Rubin, D.B.2
-
26
-
-
35348970485
-
'GeneSrF and varSelRF: A web-based tool and R package for gene selection and classification using random forest
-
Diaz-Uriarte, R. 2007. 'GeneSrF and varSelRF: a web-based tool and R package for gene selection and classification using random forest, BMC Bioinformatics, 8: 328.
-
(2007)
BMC Bioinformatics
, vol.8
, pp. 328
-
-
Diaz-Uriarte, R.1
-
28
-
-
0042434885
-
Ensemble Learning
-
(M.A. Arbib, Ed.), Cambridge, MA: The MIT Press
-
Dietterich, T. 2002. 'Ensemble Learning,' In The Handbook of Brain Theory and Neural Networks, Second edition, (M.A. Arbib, Ed.), Cambridge, MA: The MIT Press, 405-408.
-
(2002)
The Handbook of Brain Theory and Neural Networks, Second Edition
, pp. 405-408
-
-
Dietterich, T.1
-
29
-
-
0142080707
-
Ensemble Methods inMachine Learning
-
Available at:
-
Dietterich, T. 2007. 'Ensemble Methods inMachine Learning,' Available at: eecs.oregonstate.edu/~tgd/publications/mcs-ensembles.ps.gz.
-
(2007)
-
-
Dietterich, T.1
-
30
-
-
0002344794
-
Bootstrap methods: Another look at the jackknife
-
Efron, B. 1979. 'Bootstrap methods: another look at the jackknife,' The Annals of Statistics 7: 1-26.
-
(1979)
The Annals of Statistics
, vol.7
, pp. 1-26
-
-
Efron, B.1
-
31
-
-
84945737762
-
A leisurely look at the bootstrap, the jackknife, and cross-validation
-
Efron, B. and G. Gong. 1983. 'A leisurely look at the bootstrap, the jackknife, and cross-validation,' The American Statistician 37: 36-48.
-
(1983)
The American Statistician
, vol.37
, pp. 36-48
-
-
Efron, B.1
Gong, G.2
-
33
-
-
0034164230
-
Additive Logistic Regression: A Statistical View of Boosting (with discussion)
-
Friedman, J.H., T. Hastie, and R. Tibsharini. 2000. 'Additive Logistic Regression: A Statistical View of Boosting' (with discussion). Annals of Statistics 28: 337-407.
-
(2000)
Annals of Statistics
, vol.28
, pp. 337-407
-
-
Friedman, J.H.1
Hastie, T.2
Tibsharini, R.3
-
34
-
-
0035470889
-
Greedy Function Approximation: A Gradient Boosting Machine
-
Friedman, J.H., T. Hastie, and R. Tibsharini. 2001. 'Greedy Function Approximation: A Gradient Boosting Machine.' Annals of Statistics 29: 1189-1232.
-
(2001)
Annals of Statistics
, vol.29
, pp. 1189-1232
-
-
Friedman, J.H.1
Hastie, T.2
Tibsharini, R.3
-
35
-
-
0037186544
-
Stochastic Gradient Boosting
-
Friedman, J.H., T. Hastie, and R. Tibsharini. 2002. 'Stochastic Gradient Boosting.' Computational Statistics and Data Analysis 38: 4, 367-78.
-
(2002)
Computational Statistics and Data Analysis
, vol.38
, Issue.4
, pp. 367-378
-
-
Friedman, J.H.1
Hastie, T.2
Tibsharini, R.3
-
36
-
-
12144290088
-
Finite sample properties of propensity score matching and weighting estimators
-
Frölich, M. 2004. 'Finite sample properties of propensity score matching and weighting estimators,' Review of Econometrics and Statistics 86: 77-90.
-
(2004)
Review of Econometrics and Statistics
, vol.86
, pp. 77-90
-
-
Frölich, M.1
-
37
-
-
3543107923
-
Bagging Equalizes Influence
-
Grandvalet, Y. 2004. 'Bagging Equalizes Influence.' Machine Learning 55: 251-70.
-
(2004)
Machine Learning
, vol.55
, pp. 251-270
-
-
Grandvalet, Y.1
-
39
-
-
34249885738
-
Matching as Nonparametric Preprocessing for Reducing Model Dependence in Parametric Causal Inference
-
Ho, D., K. Imai, G. King, and E. Stuart. 2007. 'Matching as Nonparametric Preprocessing for Reducing Model Dependence in Parametric Causal Inference,' Political Analysis, 15: 199-236.
-
(2007)
Political Analysis
, vol.15
, pp. 199-236
-
-
Ho, D.1
Imai, K.2
King, G.3
Stuart, E.4
-
40
-
-
85057943047
-
Random Decision Forest
-
August 14-18, 1995
-
Ho, T.K. 1995. 'Random Decision Forest'. Proceedings of the 3rd Inter- national Conf. on Document Analysis and Recognition, Montreal, Canada, August 14-18, 1995, 278-282.
-
(1995)
Proceedings of the 3rd Inter- National Conf. On Document Analysis and Recognition, Montreal, Canada
, pp. 278-282
-
-
Ho, T.K.1
-
41
-
-
0037410515
-
Double-bagging: Combining classifiers by bootstrap aggregation
-
Hothorn, T. and B. Lausen. 2003. 'Double-bagging: Combining classifiers by bootstrap aggregation,' Pattern Recognition, 36: 6, 1303-1309.
-
(2003)
Pattern Recognition
, vol.36
, Issue.6
, pp. 1303-1309
-
-
Hothorn, T.1
Lausen, B.2
-
42
-
-
0346656880
-
Bagging Survival Trees
-
Hothorn, T., B. Lausen, A. Benner and Ma. Radespiel-Troeger. 2004. 'Bagging Survival Trees'. Statistics in Medicine, 23: 1, 77-91.
-
(2004)
Statistics in Medicine
, vol.23
, Issue.1
, pp. 77-91
-
-
Hothorn, T.1
Lausen, B.2
Benner, A.3
Radespiel-Troeger, M.4
-
43
-
-
33745466826
-
Survival Ensembles
-
Hothorn, T., P. Buhlmann, S. Dudoit, A. Molinaro and M.J. van der Laan. 2006. 'Survival Ensembles'. Biostatistics, 7: 3, 355-373.
-
(2006)
Biostatistics
, vol.7
, Issue.3
, pp. 355-373
-
-
Hothorn, T.1
Buhlmann, P.2
Dudoit, S.3
Molinaro, A.4
van der Laan, M.J.5
-
44
-
-
84857435462
-
-
ipred
-
Hothorn, T. and A. Peters, 2009. ipred, http://cran.r-project.org/web/packages/ipred/index.html
-
(2009)
-
-
Hothorn, T.1
Peters, A.2
-
46
-
-
84857435459
-
-
Karpievitch, Y.V., A.P. Leclerc, E.G. Hill, J.S. Almeida, 'RF++: Improved Random Forest for Clustered Data Classification,' http://www.ohloh.net/p/rfpp
-
RF++: Improved Random Forest for Clustered Data Classification
-
-
Karpievitch, Y.V.1
Leclerc, A.P.2
Hill, E.G.3
Almeida, J.S.4
-
47
-
-
84906319329
-
Forecasting Stock Index Movement: A Comparison of Support Vector Machines and Random Forest
-
Paper Available at SSRN:
-
Kumar, Manish and M. Thenmozhi, 'Forecasting Stock Index Movement: A Comparison of Support Vector Machines and Random Forest,' Indian Institute of Capital Markets 9th Capital Markets Conference Paper Available at SSRN: http://ssrn.com/abstract=876544.
-
Indian Institute of Capital Markets 9th Capital Markets Conference
-
-
Kumar, M.1
Thenmozhi, M.2
-
50
-
-
0345040873
-
Classification and Regression by randomForest
-
(Discussion of the use of the random forest package for R)
-
Liaw, A. and M. Wiener. 'Classification and Regression by randomForest' R News (2002) Vol. 2/3 p. 18 (Discussion of the use of the random forest package for R).
-
(2002)
R News
, vol.2-3
, pp. 18
-
-
Liaw, A.1
Wiener, M.2
-
53
-
-
0036556537
-
Regression TreesWith Unbiased Variable Selection and Interaction Detection
-
Loh, W.-Y. 2002. 'Regression TreesWith Unbiased Variable Selection and Interaction Detection.' Statistica Sinica 12: 361-86.
-
(2002)
Statistica Sinica
, vol.12
, pp. 361-386
-
-
Loh, W.-Y.1
-
54
-
-
84937440094
-
The Consistency of Greedy Algorithms for Classification
-
Mannor, S., R. Meir and T. Zhang. 2002. 'The Consistency of Greedy Algorithms for Classification,' COLT, 319-333.
-
(2002)
COLT
, pp. 319-333
-
-
Mannor, S.1
Meir, R.2
Zhang, T.3
-
56
-
-
84857419940
-
Outlier and Exception Analysis in Rough Sets and Granular Computing
-
(Eds. W Pedrycz, A. Skowron, V. Kreinovich), Wiley 2008
-
Nyuyen, T.T. 2008. 'Outlier and Exception Analysis in Rough Sets and Granular Computing,' in Handbook of Granular Computing (Eds. W Pedrycz, A. Skowron, V. Kreinovich), Wiley 2008.
-
(2008)
Handbook of Granular Computing
-
-
Nyuyen, T.T.1
-
57
-
-
0000551189
-
Popular Ensemble Methods: An Empirical Study
-
Opitz, D. and R. Maclin. 1999. 'Popular Ensemble Methods: An Empirical Study', Journal of Artificial Intelligence Research, 11, citeseer.ist.psu.edu/opitz99popular.html.
-
(1999)
Journal of Artificial Intelligence Research
, vol.11
, pp. 169-198
-
-
Opitz, D.1
Maclin, R.2
-
61
-
-
0001198982
-
The consequences of adjusting for a concomitant variable that has been affected by the treatment
-
Series A
-
Rosenbaum, P.R. 1984. 'The consequences of adjusting for a concomitant variable that has been affected by the treatment,' Journal of the Royal Statistical Society, Series A 147: 656-66.
-
(1984)
Journal of the Royal Statistical Society
, vol.147
, pp. 656-666
-
-
Rosenbaum, P.R.1
-
64
-
-
77951622706
-
The central role of the propensity score in observational studies for causal effects
-
Rosenbaum, P.R., and D.B. Rubin. 1983. 'The central role of the propensity score in observational studies for causal effects,' Biometrika 70: 41-55.
-
(1983)
Biometrika
, vol.70
, pp. 41-55
-
-
Rosenbaum, P.R.1
Rubin, D.B.2
-
66
-
-
0025448521
-
The strength of weak learnability
-
Schapire, R.E. 1990. 'The strength of weak learnability,' Machine Learning, 5: 197-227.
-
(1990)
Machine Learning
, vol.5
, pp. 197-227
-
-
Schapire, R.E.1
-
68
-
-
0032280519
-
Boosting the margin: A new explanation for the effectiveness of voting methods
-
Schapire, R.E., Y. Freund, P. Bartlett, and W.S. Lee. 1998. 'Boosting the margin: A new explanation for the effectiveness of voting methods,' The Annals of Statistics, 26: 1651-1686.
-
(1998)
The Annals of Statistics
, vol.26
, pp. 1651-1686
-
-
Schapire, R.E.1
Freund, Y.2
Bartlett, P.3
Lee, W.S.4
-
70
-
-
16444381830
-
Tumor classification by tissue microarray profiling: Random forest clustering applied to renal cell carcinoma
-
Shi, T., Seligson, D., Belldegrun, A.S., Palotie, A. and Horvath, S. 2005. 'Tumor classification by tissue microarray profiling: random forest clustering applied to renal cell carcinoma,' Modern Pathology 18: 4, 547-57.
-
(2005)
Modern Pathology
, vol.18
, Issue.4
, pp. 547-557
-
-
Shi, T.1
Seligson, D.2
Belldegrun, A.S.3
Palotie, A.4
Horvath, S.5
-
72
-
-
33847096395
-
Bias in RandomForest Variable Importance Measures: Illustrations, Sources and a Solution
-
Strobl, C., A. Boulesteix, A. Zeileis and T. Hothorn. 2007. Bias in RandomForest Variable Importance Measures: Illustrations, Sources and a Solution. BMC Bioinformatics, 8, 25. http://www.biomedcentral.com/1471-2105/8/25/abstract.
-
(2007)
BMC Bioinformatics
, vol.8
, pp. 25
-
-
Strobl, C.1
Boulesteix, A.2
Zeileis, A.3
Hothorn, T.4
-
73
-
-
71249149634
-
Danger: High Power! - Exploring the Statistical Properties of a Test for Random Forest Variable Importance
-
Department of Statistics, University of Munich
-
Strobl, C. and A. Zeileis. 2008. 'Danger: High Power! - Exploring the Statistical Properties of a Test for Random Forest Variable Importance,' Technical Report Number 017, Department of Statistics, University of Munich.
-
(2008)
Technical Report Number 017
-
-
Strobl, C.1
Zeileis, A.2
-
74
-
-
48549095457
-
Conditional variable importance for Random Forests
-
Strobl, C., A-L Boulesteix, T. Augustin and A. Zeileis. 2008. 'Conditional variable importance for Random Forests,' BMC Bioinformat- ics, 9: 307.
-
(2008)
BMC Bioinformat- Ics
, vol.9
, pp. 307
-
-
Strobl, C.1
Boulesteix, A.-L.2
Augustin, T.3
Zeileis, A.4
-
75
-
-
0000388992
-
Consistent nonparametric regression
-
Stone, C. 1977. 'Consistent nonparametric regression,' The Annals of Statistics, 5: 595-645.
-
(1977)
The Annals of Statistics
, vol.5
, pp. 595-645
-
-
Stone, C.1
-
78
-
-
84857389556
-
-
STAT900 Slides, University of Pennsylvania, Nov. 26
-
Traskin, M. 'Random Forests: classification, variable selection and consistency,' STAT900 Slides, University of Pennsylvania, Nov. 26, 2007.
-
(2007)
Random Forests: Classification, Variable Selection and Consistency
-
-
Traskin, M.1
-
79
-
-
84857435467
-
-
Available at:
-
Wang, T. MATLAB R13. Available at: http://lib.stat.cmu.edu/matlab/
-
MATLAB R13
-
-
Wang, T.1
-
80
-
-
32544435787
-
Short-term prediction of mortality in patients with systemic lupus erythematosus: Classification of outcomes using Random Forests
-
Ward, M., S. Pajevic, J. Dreyfuss, and J. Malley. 2006. 'Short-term prediction of mortality in patients with systemic lupus erythematosus: classification of outcomes using Random Forests,' Arthritis and Rheumatism 55: 74-80.
-
(2006)
Arthritis and Rheumatism
, vol.55
, pp. 74-80
-
-
Ward, M.1
Pajevic, S.2
Dreyfuss, J.3
Malley, J.4
|