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This article is cited in 2 scientific papers (total in 2 papers)
On performance of boosting in classification problem
V. M. Nedelkoab a Novosibirsk State University
b Sobolev Institute of Mathematics, Siberian Branch of the Russian Academy of Sciences, Novosibirsk
Abstract:
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.
Received: 15.11.2014
Citation:
V. M. Nedelko, “On performance of boosting in classification problem”, Vestn. Novosib. Gos. Univ., Ser. Mat. Mekh. Inform., 15:2 (2015), 72–89
Linking options:
https://www.mathnet.ru/eng/vngu369 https://www.mathnet.ru/eng/vngu/v15/i2/p72
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Abstract page: | 295 | Full-text PDF : | 169 | References: | 59 | First page: | 14 |
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