article · 2008

Abstract ‘Modest AdaBoost ’ – Teaching AdaBoost to Generalize Better

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Abstract

Boosting is a technique of combining a set weak classifiers to form one high-performance prediction rule. Boosting was successfully applied to solve the problems of object detection, text analysis, data mining and etc. The most and widely used boosting algorithm is AdaBoost and its later more effective variations Gentle and Real AdaBoost. In this article we propose a new boosting algorithm, which produces less generalization error compared to mentioned algorithms at the cost of somewhat higher training error.

Cite this paper

APA 7

(2008). Abstract ‘Modest AdaBoost ’ – Teaching AdaBoost to Generalize Better.

MLA 9

“Abstract ‘Modest AdaBoost ’ – Teaching AdaBoost to Generalize Better.” 2008.

Chicago

2008. “Abstract ‘Modest AdaBoost ’ – Teaching AdaBoost to Generalize Better.”

Harvard

(2008) ‘Abstract ‘Modest AdaBoost ’ – Teaching AdaBoost to Generalize Better’.

BibTeX

@article{anon2008abstract,
  title = {{Abstract ‘Modest AdaBoost ’ – Teaching AdaBoost to Generalize Better}},
  year = {2008}
}

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