em_dd,divide_10fold_Musk1
2016-08-23
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%This function uses Bagging[1] paradigm to build ensemble of Diverse Density learners.
%The Musk1 data is partitioned into ten folds using function 'divide_10fold_Musk1' where all the resulting files are stored in 'curdir', in each fold, Bagging is used
%to build an ensemble for Diverse Density learners. Each ensemble comprises five versions of the based multi-instance learner.
%
% For more details of Ensembles of Multi-Instance Learners, please refer to bibliography [2]
% [1] L. Breiman. Bagging predictors. Machine Learning, 24(2): 123-140, 1996.
% [2] Z.-H. Zhou and M.-L. Zhang. Ensembles of multi-instance learners. In: Lecture Notes in Computer Science 2837, Berlin: Springer-Verlag, 2003, 492-502.
%The Musk1 data is partitioned into ten folds using function 'divide_10fold_Musk1' where all the resulting files are stored in 'curdir', in each fold, Bagging is used
%to build an ensemble for Diverse Density learners. Each ensemble comprises five versions of the based multi-instance learner.
%
% For more details of Ensembles of Multi-Instance Learners, please refer to bibliography [2]
% [1] L. Breiman. Bagging predictors. Machine Learning, 24(2): 123-140, 1996.
% [2] Z.-H. Zhou and M.-L. Zhang. Ensembles of multi-instance learners. In: Lecture Notes in Computer Science 2837, Berlin: Springer-Verlag, 2003, 492-502.
matlab
emdddividefoldMusk
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