Looking inside self-organizing map ensembles with resampling and negative correlation learning
type: Item
instance of: scholarly article
title: Looking inside self-organizing map ensembles with resampling and negative correlation learning
zbMATH Open document ID: 1217.68186
author: Alexandra Scherbart
author: Tim W. Nattkemper
published in: Neural Networks
publication date: 2011-07-26T00:00:00Z
Mathematics Subject Classification ID: 68T05
zbMATH DE Number: 5932164
zbMATH Keywords: ensemble learning
zbMATH Keywords: self-organizing maps
zbMATH Keywords: negative correlation learning
zbMATH Keywords: regression
zbMATH Keywords: bagging
zbMATH Keywords: random subspace method
describes a project that uses: R
describes a project that uses: e1071
describes a project that uses: StatLib Datasets Archive
describes a project that uses: AAindex
describes a project that uses: UCI-ml
describes a project that uses: pls
describes a project that uses: randomForest
describes a project that uses: ElemStatLearn
MaRDI profile type: Publication
full work available at URL: j.neunet.2010.08.004
OpenAlex ID: W2066354979
Wikidata QID: Q51660521
cites work: Random forests
cites work: Bagging predictors
cites work: Q3093311
cites work: Locally Weighted Regression: An Approach to Regression Analysis by Local Fitting
cites work: Multivariate adaptive regression splines
cites work: Q4783979
cites work: The elements of statistical learning. Data mining, inference, and prediction
cites work: Q4444136
cites work: Self-organized formation of topologically correct feature maps
cites work: Negative correlation in incremental learning
DOI: 10.1016/J.NEUNET.2010.08.004
description: scientific article; zbMATH DE number 5932164