摘要: In recent years, together with bagging [5] and the random subspace method [15], boosting [6] became one of most popular combining techniques that allows us to improve a weak classifier. Usually, is applied Decision Trees (DT's). this paper, we study in Linear Discriminant Analysis (LDA). Simulation studies, carried out for artificial data set two real sets, show might be useful LDA large training sample sizes while critical [11]. contrast common opinion, demonstrate usefulness does not depend on instability
springer.com
core.ac.uk
tudelft.nl 
researchgate.net 参考文章(18)
A.K. Jain, B. Chandrasekaran, 39 Dimensionality and sample size considerations in pattern recognition practice Handbook of Statistics. ,vol. 2, pp. 835- 855 ,(1982) , 10.1016/S0169-7161(82)02042-2
Marina Skurichina, Robert P. W. Duin, The Role of Combining Rules in Bagging and Boosting Lecture Notes in Computer Science. pp. 631- 640 ,(2000) , 10.1007/3-540-44522-6_65
Thomas G. Dietterich, An Experimental Comparison of Three Methods for Constructing Ensembles of Decision Trees: Bagging, Boosting, and Randomization Machine Learning. ,vol. 40, pp. 139- 157 ,(2000) , 10.1023/A:1007607513941
Keinosuke Fukunaga, Introduction to statistical pattern recognition (2nd ed.) Academic Press Professional, Inc.. ,(1990)
Robert E. Schapire, Yoav Freund, Peter Bartlett, Wee Sun Lee, Boosting the margin: a new explanation for the effectiveness of voting methods Annals of Statistics. ,vol. 26, pp. 1651- 1686 ,(1998) , 10.1214/AOS/1024691352
Robert J Tibshirani, Bradley Efron, An introduction to the bootstrap ,(1993)
Jerome Friedman, Trevor Hastie, Robert Tibshirani, Additive logistic regression: a statistical view of boosting (With discussion and a rejoinder by the authors) Annals of Statistics. ,vol. 28, pp. 337- 407 ,(2000) , 10.1214/AOS/1016218223
Jerome H. Friedman, Regularized Discriminant Analysis Journal of the American Statistical Association. ,vol. 84, pp. 165- 175 ,(1989) , 10.1080/01621459.1989.10478752
Marina Skurichina, Robert P.W. Duin, BAGGING FOR LINEAR CLASSIFIERS Pattern Recognition. ,vol. 31, pp. 909- 930 ,(1998) , 10.1016/S0031-3203(97)00110-6
Leo Breiman, Arcing classifier (with discussion and a rejoinder by the author) Annals of Statistics. ,vol. 26, pp. 801- 849 ,(1998) , 10.1214/AOS/1024691079