adabag: AnRPackage for Classification with Boosting and Bagging
نویسندگان
چکیده
منابع مشابه
Writer Demographic Classification Using Bagging and Boosting
Classifying handwriting into a writer demographic category, e.g., gender, age, or handedness of the writer, is useful for more detailed analysis such as writer verification and identification. This paper describes classification into binary demographic categories using document macro features and several different classification methods: a single feed-forward neural network classifier and combi...
متن کاملBagging, Boosting, and C4.5
Breiman's bagging and Freund and Schapire's boosting are recent methods for improving the predictive power of classiier learning systems. Both form a set of classiiers that are combined by voting, bagging by generating replicated boot-strap samples of the data, and boosting by adjusting the weights of training instances. This paper reports results of applying both techniques to a system that le...
متن کاملCombining Bagging and Boosting
Bagging and boosting are among the most popular resampling ensemble methods that generate and combine a diversity of classifiers using the same learning algorithm for the base-classifiers. Boosting algorithms are considered stronger than bagging on noisefree data. However, there are strong empirical indications that bagging is much more robust than boosting in noisy settings. For this reason, i...
متن کاملParallelizing Boosting and Bagging
Bagging and boosting are two general techniques for building predictors based on small samples from a dataset. We show that boosting can be parallelized, and then present performance results for parallelized bagging and boosting using OC1 decision trees and two standard datasets. The main results are that sample sizes limit achievable accuracy, regardless of computational time spent; that paral...
متن کاملBagging Boosting and C
Breiman s bagging and Freund and Schapire s boosting are recent methods for improving the predictive power of classi er learning systems Both form a set of classi ers that are combined by voting bagging by generating replicated boot strap samples of the data and boosting by ad justing the weights of training instances This paper reports results of applying both techniques to a system that learn...
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ژورنال
عنوان ژورنال: Journal of Statistical Software
سال: 2013
ISSN: 1548-7660
DOI: 10.18637/jss.v054.i02