Skip to search formSkip to main contentSkip to account menu

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… 
Wikipedia (opens in a new tab)

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.
Highly Cited
2015
Highly Cited
2015
Time series classification with ensembles of elastic distance measures
Several alternative distance measures for comparing time series have recently been proposed and evaluated on time series… 
Highly Cited
2012
Highly Cited
2012
Bagging, Boosting and Ensemble Methods
Ensemble methods aim at improving the predictive performance of a given statistical learning or model fitting technique. The… 
Highly Cited
2010
Highly Cited
2010
Ensemble Based Extreme Learning Machine
Extreme learning machine (ELM) was proposed as a new class of learning algorithm for single-hidden layer feedforward neural… 
Review
2010
Review
2010
Ensemble Methods in Data Mining: Improving Accuracy Through Combining Predictions
Ensemble methods have been called the most influential development in Data Mining and Machine Learning in the past decade. They… 
Highly Cited
2009
Highly Cited
2009
Research on Ensemble Learning
Ensemble learning is a powerful machine learning paradigm which has exhibited apparent advantages in many applications. An… 
Highly Cited
2009
Highly Cited
2009
Pattern Classification Using Ensemble Methods
Researchers from various disciplines such as pattern recognition, statistics, and machine learning have explored the use of… 
Highly Cited
2006
Highly Cited
2006
Bayesian Ensemble Learning
We develop a Bayesian "sum-of-trees" model, named BART, where each tree is constrained by a prior to be a weak learner. Fitting… 
Review
2004
Review
2004
Classifier Ensembles for Changing Environments
We consider strategies for building classifier ensembles for non-stationary environments where the classification task changes… 
Highly Cited
2000
Highly Cited
2000
Online Ensemble Learning: An Empirical Study
We study resource-limited online learning, motivated by the problem of conditional-branch outcome prediction in computer… 
Highly Cited
1999
Highly Cited
1999
A Variational Bayesian Framework for Graphical Models
This paper presents a novel practical framework for Bayesian model averaging and model selection in probabilistic graphical… 
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)