Synthetic learning machines
Tóm tắt
Từ khóa
Tài liệu tham khảo
Biau G, Fischer A, Guedj B, Malley JD: COBRA: a non-linear aggregation strategy. Paris – France: Technical Report, Université Pierre et Marie Curie; 2013:1–27. [[ http://www.lsta.upmc.fr/BIAU/publications.html ]]
Vapnik V: Statistical Learning Theory. 1998, Wiley, New York
Tibshirani RJ:Regression shrinkage and selection via the lasso. J R Stat Soc Series B. 1996, 58: 267-288.
Cover TM, Hart PE:Nearest neighbor pattern classification. IEEE Trans Inform Theory. 1967, IT-13: 21-27. 10.1109/TIT.1967.1053964.
Pan Q, Hu T, Malley J, Andrew A, Karagas M, Moore J:A system-level pathway-phenotype association analysis using synthetic feature random forest. Genet Epidemiol. 2014, 38 (3): 209-219. 10.1002/gepi.21794.
Biau G, Devroye L, Lugosi G:Consistency of random forests and other averaging classifiers. J Mach Learn Res. 2008, 9: 2015-2033. [http://doi.acm.org/10.1145/1390681.1442799],
Ishwaran H, Kogalur UB, Chen X, Minn AJ:Random survival forests for high-dimensional data. Stat Anal Data Mining. 2011, 4: 115-132. 10.1002/sam.10103. [http://dx.doi.org/10.1002/sam.10103],
Lin Y, Jeon Y:Random forests and adaptive nearest neighbors. J Am Stat Assoc. 2006, 101 (474): 578-590. 10.1198/016214505000001230.
Ishwaran H, Kogalur U: Random forests for survival, regression and classification (RF-SRC), R package version 1.5.52014. [ http://cran.r-project.org/web/packages/randomForestSRC/index.html ]
Classification and regression by randomforest. R News. 2002, 2 (3): 18-22.
Guedj B: COBRA: nonlinear aggregation of predictors. R package version 0.99.42013. [ http://cran.r-project.org/web/packages/COBRA/index.html ]
Leisch F, Dimitriadou E: mlbench: machine learning benchmark problems. R package version 2.1-12012. [ http://cran.r-project.org/web/packages/mlbench/index.html ]
Demsar J:Statistical comparisons of classifiers over multiple data sets. J Mach Learn Res. 2006, 7: 1-30. [http://www.jmlr.org/papers/v7/demsar06a.html],