Skip to search formSkip to main contentSkip to account menu

AdaBoost

Known as: Adaboosting 
AdaBoost, short for "Adaptive Boosting", is a machine learning meta-algorithm formulated by Yoav Freund and Robert Schapire who won the Gödel Prize… 
Wikipedia (opens in a new tab)

Related topics

Related topics

34 relations
Adaptive algorithmBackfitting algorithmBackpropagationBag-of-words model in computer vision

Broader (1)

Ensemble learning

Papers overview

Semantic Scholar uses AI to extract papers important to this topic.
Review
2013
Review
2013
Explaining AdaBoost
Boosting is an approach to machine learning based on the idea of creating a highly accurate prediction rule by combining many… 
Highly Cited
2006
Highly Cited
2006
Aggregate features and ADABOOST for music classification
We present an algorithm that predicts musical genre and artist from an audio waveform. Our method uses the ensemble learner… 
Highly Cited
2006
Highly Cited
2006
Recognition and Segmentation of 3-D Human Action Using HMM and Multi-class AdaBoost
Our goal is to automatically segment and recognize basic human actions, such as stand, walk and wave hands, from a sequence of… 
Highly Cited
2006
Highly Cited
2006
AdaBoost is Consistent
The risk, or probability of error, of the classifier produced by the AdaBoost algorithm is investigated. In particular, we… 
Highly Cited
2005
Highly Cited
2005
Supervised Learning of Places from Range Data using AdaBoost
This paper addresses the problem of classifying places in the environment of a mobile robot into semantic categories. We believe… 
Highly Cited
2005
Highly Cited
2005
‘ Modest AdaBoost ’ – Teaching AdaBoost to Generalize Better
Boosting is a technique of combining a set weak classifiers to form one high-performance prediction rule. Boosting was… 
Highly Cited
2004
Highly Cited
2004
Fast rotation invariant multi-view face detection based on real Adaboost
In this paper, we propose a rotation invariant multi-view face detection method based on Real Adaboost algorithm. Human faces are… 
Highly Cited
2001
Highly Cited
2001
Soft Margins for AdaBoost
Recently ensemble methods like ADABOOST have been applied successfully in many problems, while seemingly defying the problems of… 
Highly Cited
2001
Highly Cited
2001
Fast and Robust Classification using Asymmetric AdaBoost and a Detector Cascade
This paper develops a new approach for extremely fast detection in domains where the distribution of positive and negative… 
Highly Cited
2000
Highly Cited
2000
Logistic Regression, AdaBoost and Bregman Distances
We give a unified account of boosting and logistic regression in which each learning problem is cast in terms of optimization of… 
By clicking accept or continuing to use the site, you agree to the terms outlined in our Privacy Policy (opens in a new tab), Terms of Service (opens in a new tab), and Dataset License (opens in a new tab)