It is shown that bagging, a computationally intensive method, asymptotically improves the performance of nearest neighbour classifiers provided that the resample size is less than 69% of the actual sample size, in the case of with-replacement bagging, o
Real-world classification data usually contain noise, which can affect the accuracy of the models an...
Bagging is a popular method that improves the classification accuracy for any learning algorithm. A ...
This is an electronic version of the paper presented at the 22th European Symposium on Artificial Ne...
Summary. It is shown that bagging, a computationally intensive method, asymptotically im-proves the ...
A formula is derived for the exact computation of Bagging classifiers when the base model adopted is...
Bagging is an ensemble learning method that has proved to be a useful tool in the arsenal of machine...
Abstract. Bagging is a simple and robust classification algorithm in the presence of class label noi...
In this paper, we propose lazy bagging (LB), which builds bootstrap replicate bags based on the char...
Bagging has been found to be successful in increasing the predictive performance of unstable classif...
Bagging forms a committee of classifiers by bootstrap aggregation of training sets from a pool of tr...
Bagging has been found to be successful in increasing the predictive performance of unstable classif...
International audienceBagging is a simple way to combine estimates in order to improve their perform...
Bagging is a device intended for reducing the prediction error of learning algorithms. In its simple...
We apply an analytical framework for the analysis of linearly combined classifiers to ensembles gene...
We propose a simple sequential procedure for bagged classification, which modifies nonparametric bag...
Real-world classification data usually contain noise, which can affect the accuracy of the models an...
Bagging is a popular method that improves the classification accuracy for any learning algorithm. A ...
This is an electronic version of the paper presented at the 22th European Symposium on Artificial Ne...
Summary. It is shown that bagging, a computationally intensive method, asymptotically im-proves the ...
A formula is derived for the exact computation of Bagging classifiers when the base model adopted is...
Bagging is an ensemble learning method that has proved to be a useful tool in the arsenal of machine...
Abstract. Bagging is a simple and robust classification algorithm in the presence of class label noi...
In this paper, we propose lazy bagging (LB), which builds bootstrap replicate bags based on the char...
Bagging has been found to be successful in increasing the predictive performance of unstable classif...
Bagging forms a committee of classifiers by bootstrap aggregation of training sets from a pool of tr...
Bagging has been found to be successful in increasing the predictive performance of unstable classif...
International audienceBagging is a simple way to combine estimates in order to improve their perform...
Bagging is a device intended for reducing the prediction error of learning algorithms. In its simple...
We apply an analytical framework for the analysis of linearly combined classifiers to ensembles gene...
We propose a simple sequential procedure for bagged classification, which modifies nonparametric bag...
Real-world classification data usually contain noise, which can affect the accuracy of the models an...
Bagging is a popular method that improves the classification accuracy for any learning algorithm. A ...
This is an electronic version of the paper presented at the 22th European Symposium on Artificial Ne...
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