AdaBoost (short for “Adaptive Boosting”) is a popular boosting classification algorithm. The AdaBoost algorithm performs well on a variety of data sets except some noisy data ([Friedman98], [Zhu2005]).
In this paper, we present a novel large-margin loss function to directly design multiclass classifier. The resulting risk, which guarantees Bayes consistency ...
Abstract —A large number of practical domains, such as scene classification and object recognition, have involved more than two classes.
In this paper, we present a novel large-margin loss function to directly design multiclass classifier. The resulting risk, which guarantees Bayes consistency ...
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AdaBoost (short for “Adaptive Boosting”) is a popular boosting classification algorithm. The AdaBoost algorithm performs well on a variety of data sets ...
The core principle of AdaBoost (Adaptive Boosting) is to fit a sequence of weak learners (e.g. Decision Trees) on repeatedly re-sampled versions of the data.
AdaBoost, short for Adaptive Boosting, is a statistical classification meta-algorithm formulated by Yoav Freund and Robert Schapire in 1995.
Sep 1, 2020 · The experimental studies show that the CatBoost and LogitBoost algorithms are superior to other boosting algorithms on multi-class imbalanced conventional and ...
Feb 20, 2023 · In this paper, an improved multi-class imbalanced data classification framework is proposed by combining the Focal Loss with Boosting model (FL-Boosting).
AdaBoost (adaptive boosting) is an ensemble learning algorithm that can be used for classification or regression.