[1] Hansen L.K., Salamon P.. Neural network ensembles [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence 1990, 10(10).
[2] KROGH A, VEDELSBY J. Neural network ensembles, cross validation, and active learning [M]. Cambridge, MA:The MIT Press 1995.
[3] DIETTERICH T G. An experimental comparison of three methods for constructing ensembles of decision tress:Bagging, boosting and randomization [J]. Machine Learning 2000, 40(02).
[4] BREIMAN L. Bagging predictors [J]. Machine Learning 1996, 24(02).
[5] OPITZ D. Feature Selection for Ensembles [A]. Orlando 1999.
[6] SCHAPIRE R E. The boosting approach to machine learning:An overview [A]. Berkeley, California, USA 2001.
[7] DIETTERICH T G, BAKIRI G. Error-correcting output codes:A general method for improving multiclass inductive learning programs [A]. Anaheim 1991.
[8] DIETTERICH T G, BAKIRI G. Solving multiclass learning problems via error correcting output codes [J]. Journal of Artificial Intelligence Research 1995, 2.
[9] MUNRO P W, PARMANTO B. Competition Among Networks Improves Committee Performance [M]. Cambridge, MA:The MIT Press 1997.
[10] Zhi-Hua Zhou, Jianxin Wu, Wei Tang. Ensembling neural networks: Many could be better than all [J]. Artificial Intelligence: An International Journal 2002, 1/2(1/2).
[11] Sharma A, Paliwal KK, Onwubolu GC. Class-dependent PCA, MDC and LDA: A combined classifier for pattern classification [J]. Pattern Recognition: The Journal of the Pattern Recognition Society 2006, 7(7).
[12] LAM L, SUE C. Optimal combination of pattern classifiers [J]. Pattern Recognition Letters 1995, 16(09).
[13] Huang Y.S., Suen C.Y.. A method of combining multiple experts for the recognition of unconstrained handwritten numerals [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence 1995, 1(1).
[14] ROGOVA G. Combining the results of several neural networks classifiers [J]. Neural Networks 1994, 7(05).
[15] Kuncheva LI., Duin RPW., Bezdek JC.. Decision templates for multiple classifier fusion: an experimental comparison [J]. Pattern Recognition: The Journal of the Pattern Recognition Society 2001, 2(2).
[16] Kittler J., Hatef M.. On combining classifiers [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence 1998, 3(3).
[17] PARMANTO B, MUNRO P, DOYLE H. Improving committee diagnosis with resampling techniques [M]. Cambridge, MA:The MIT Press 1996.
[18] PARMANTO B, MUNRO P, DOYLE H. Reducing variance of committee prediction with resampling techniques [J]. CONNECTION SCIENCE 1996, 8(3-4).
[19] CUNNINGHAM P, CARNEY J. Diversity versus quality in classification ensemble based on feature selection [A]. Spain:Barcelona 2000.
[20] Anand R., Mehrotra K.. Efficient classification for multiclass problems using modular neural networks [J]. IEEE Transactions on Neural Networks 1995, 1(1).
[21] HASTIE T, TIBSHIRANI R. Classification by pairwise coupling [J]. Annals of Statistics 1998, 26(01).
[22] Giorgio Giacinto, Fabio Roli. An approach to the automatic design of multiple classifier systems [J]. Pattern recognition letters 2001, 1(1). |
