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Bootstrap aggregating

Known as: Bootstrap aggregation, Bootstrapped Aggregation, Bootstrapping (machine learning) 
Bootstrap aggregating, also called bagging, is a machine learning ensemble meta-algorithm designed to improve the stability and accuracy of machine… 
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Related topics

Related topics

22 relations
AdaBoostBias–variance tradeoffBoosting (machine learning)Bootstrapping (statistics)

Broader (1)

Computational statistics

Papers overview

Semantic Scholar uses AI to extract papers important to this topic.
2018
2018
Enhancing Prediction Accuracy of Default of Credit Using Ensemble Techniques
Credit rating of an institution or individual provides a suggestive financial picture and strength of the individual or the… 
2017
2017
Multiple-instance ensemble learning for hyperspectral images
Abstract. An ensemble framework for multiple-instance (MI) learning (MIL) is introduced for use in hyperspectral images (HSIs) by… 
2016
2016
Ensemble Tree Learning Techniques for Magnetic Resonance Image Analysis
This paper shows a comparative study of boosting and bagging algorithms for magnetic resonance image (MRI) analysis and… 
2015
2015
Selecting the Appropriate Ensemble Learning Approach for Balanced Bioinformatics Data
Ensemble learning (process of combining multiple models into a single decision) is an effective tool for improving the… 
2011
2011
Shortipedia aggregating and curating Semantic Web data
2008
2008
Constraint Projections for Ensemble Learning
It is well-known that diversity among base classifiers is crucial for constructing a strong ensemble. Most existing ensemble… 
2004
2004
A new algorithm to build consolidated trees: study of the error rate and steadiness
This paper presents a new methodology for building decision trees, Consolidated Trees Construction algorithm, that improves the… 
2003
2003
Texture classification of logged forests in tropical Africa using machine-learning algorithms
This Letter describes a procedure that incorporates textural measures in the classification of logged forests from Landsat… 
2000
2000
Bagging and Boosting a Treebank Parser
Bagging and boosting, two effective machine learning techniques, are applied to natural language parsing. Experiments using these… 
1995
1995
A Comparison of Methods for Learning and Combining Evidence From Multiple Models
Most previous work on multiple models has been done on a few domains. We present a com-parsion of three ways of learning multiple… 
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