Geodesic
On performance of boosting in classification problem
Sibirskij žurnal čistoj i prikladnoj matematiki, Tome 15 (2015) no. 2, pp. 72-89

Voir la notice de l'article provenant de la source Math-Net.Ru

The work provide some new explanation of effectiveness of the boosting methods. The main reason why boosting makes good decision functions on real world tasks is that the boosting utilizes some pattern of feature independence. We also discuss margin based risk estimations with relation to boosting and show that margin depends on complexity of composition.
Keywords: boosting, pattern recognition, machine learning, margin, misclassification probability. 
@article{VNGU_2015_15_2_a5,
     author = {V. M. Nedelko},
     title = {On performance of boosting in classification problem},
     journal = {Sibirskij \v{z}urnal \v{c}istoj i prikladnoj matematiki},
     pages = {72--89},
     publisher = {mathdoc},
     volume = {15},
     number = {2},
     year = {2015},
     language = {ru},
     url = {http://geodesic.mathdoc.fr/item/VNGU_2015_15_2_a5/}
}
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V. M. Nedelko. On performance of boosting in classification problem. Sibirskij žurnal čistoj i prikladnoj matematiki, Tome 15 (2015) no. 2, pp. 72-89. http://geodesic.mathdoc.fr/item/VNGU_2015_15_2_a5/