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.
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Which algorithm to use for multiclass classification?
What is the AdaBoost algorithm for classification?
Can AdaBoost be used for multiclass?
When to use adaptive boosting?
AdaBoost (short for “Adaptive Boosting”) is a popular boosting classification algorithm. The AdaBoost algorithm performs well on a variety of data sets except ...
AdaBoost (short for “Adaptive Boosting”) is a popular boosting classification algorithm. The AdaBoost algorithm performs well on a variety of data sets ...
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 ...
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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.
Algorithms that are adaptive, such as AdaBoost, are much more practical because they do not require such prior information.
Sep 3, 2020 · The proposed AdaBoost-CNN is designed to reduce the computational cost of the classical AdaBoost when dealing with large sets of training data.
In this paper, the capability of Adaptive Boosting (AdaBoost) is integrated with a Convolutional Neural Network (CNN) to design a new machine learning method, ...
A novel multiclass classification algorithm Gentle Adaptive Multiclass Boosting Learning (GAMBLE) is proposed to address these issues. The algorithm ...