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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.
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… 
2016
2016
Inverse modeling of non-cooperative agents via mixture of utilities
We describe a new method of parametric utility learning for non-cooperative, continuous games using a probabilistic… 
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
Highly Cited
2009
Highly Cited
2009
Reducing Semantic Drift with Bagging and Distributional Similarity
Iterative bootstrapping algorithms are typically compared using a single set of hand-picked seeds. However, we demonstrate that… 
2009
2009
HETEROGENEOUS ENSEMBLE CLASSIFICATION
The problem of multi-class classication is explored using heterogeneous ensemble classiers. Heterogeneous ensembles classiers are… 
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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