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LPBoost

Linear Programming Boosting (LPBoost) is a supervised classifier from the boosting family of classifiers. LPBoost maximizes a margin between training… 
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Related topics

Related topics

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AdaBoostBoosting (machine learning)Decision stumpEnsemble learning

Papers overview

Semantic Scholar uses AI to extract papers important to this topic.
2018
2018
Classifier Ensemble by Exploring Supplementary Ordering Information
Supplementary information has been proven to be particularly useful in many machine learning tasks. In ensemble learning for a… 
Review
2016
Review
2016
Earthquake magnitude prediction based on artificial neural networks: A survey
The occurrence of earthquakes has been studied from many aspects. Apparently, earthquakes occur without warning and can devastate… 
2016
2016
An integrated intrusion detection model using consistency based feature selection and LPBoost
Most of the intrusion detection systems analyze all network traffic features to identify intrusions with different classification… 
2015
2015
Progressive 3D shape segmentation using online learning
2013
2013
Direct 0-1 Loss Minimization and Margin Maximization with Boosting
We propose a boosting method, DirectBoost, a greedy coordinate descent algorithm that builds an ensemble classifier of weak… 
2012
2012
Selective Boosting Algorithm for Maximizing the Soft Margin
Research of traditional boosting algorithms mainly focuses on maximizing the hard or soft margin of the convex combination among… 
2010
2010
The optimization of sensor arrangement and feature selection in activity recognition
This paper deals with the optimization of sensor arrangement and feature selection for activity recognition of the people living… 
2008
2008
Boosting the Minimum Margin: LPBoost vs. AdaBoost
LPBoost seemingly should have better generalization capability than AdaBoost according to the margin theory (Schapire, 1999… 
2007
2007
Boosting, Margins and Beyond
2005
2005
Semi-supervised mixture of kernels via LPBoost methods
We propose an algorithm to construct classification models with a mixture of kernels from labeled and unlabeled data. The derived… 
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