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Ensemble learning

Known as: Ensemble Algorithms, Ensemble Methods, Ensemble 
In statistics and machine learning, ensemble methods use multiple learning algorithms to obtain better predictive performance than could be obtained… 
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Related topics

Related topics

34 relations
Anomaly detectionBackpropagationBayesian structural time seriesBias–variance tradeoff

Papers overview

Semantic Scholar uses AI to extract papers important to this topic.
2013
2013
DUET: integration of dynamic and static analyses for malware clustering with cluster ensembles
Automatic malware clustering plays a vital role in combating the rapidly growing number of malware variants. Most existing… 
2013
2013
Artificial neural network based on rotation forest for biomedical pattern classification
The novel classifier system based on ensemble classifier is proposed in this paper. Rotation forest algorithm based on principal… 
2012
2012
Similarity-based spectral clustering ensemble selection
Traditional clustering ensemble methods combine all the obtained clustering results at hand. However, we can often achieve a… 
2011
2011
Can matrix coherence be efficiently and accurately estimated?
Matrix coherence has recently been used to characterize the ability to extract global information from a subset of matrix entries… 
2009
2009
A Multi-partition Multi-chunk Ensemble Technique to Classify Concept-Drifting Data Streams
We propose a multi-partition, multi-chunk ensemble classifier based data mining technique to classify concept-drifting data… 
Highly Cited
2007
Highly Cited
2007
On-Line Ensemble SVM for Robust Object Tracking
In this paper, we present a novel visual object tracking algorithm based on ensemble of linear SVM classifiers. There are two… 
Review
2007
Review
2007
Movie Review Classification Based on a Multiple Classifier
In this paper, we propose a method to classify movie review documents into positive or negative opinions. There are several… 
Highly Cited
2005
Highly Cited
2005
Sensitivity analysis and assessment of prior model probabilities in MLBMA with application to unsaturated fractured tuff
Previous application of maximum likelihood Bayesian model averaging (MLBMA, Neuman (2002, 2003)) to alternative variogram models… 
2002
2002
Option pricing under model and parameter uncertainty using predictive densities
The theoretical price of a financial option is given by the expectation of its discounted expiry time payoff. The computation of… 
2002
2002
Accounting for conceptual model uncertainty via maximum likelihood Bayesian model averaging
Analyses of groundwater flow and transport typically rely on a single conceptual model of site hydrogeology. Yet hydrogeological… 
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