Edwin Lughofer
Citado por
Citado por
Central moment discrepancy (cmd) for domain-invariant representation learning
W Zellinger, T Grubinger, E Lughofer, T Natschläger, S Saminger-Platz
arXiv preprint arXiv:1702.08811, 2017
Evolving fuzzy systems-methodologies, advanced concepts and applications
E Lughofer
Springer, 2011
FLEXFIS: A robust incremental learning approach for evolving Takagi–Sugeno fuzzy models
ED Lughofer
IEEE Transactions on fuzzy systems 16 (6), 1393-1410, 2008
Evolving fuzzy and neuro-fuzzy approaches in clustering, regression, identification, and classification: A survey
I Škrjanc, JA Iglesias, A Sanchis, D Leite, E Lughofer, F Gomide
Information sciences 490, 344-368, 2019
PANFIS: A novel incremental learning machine
M Pratama, SG Anavatti, PP Angelov, E Lughofer
IEEE Transactions on Neural Networks and Learning Systems 25 (1), 55-68, 2013
Evolving fuzzy classifiers using different model architectures
P Angelov, E Lughofer, X Zhou
Fuzzy sets and systems 159 (23), 3160-3182, 2008
Learning in non-stationary environments: methods and applications
M Sayed-Mouchaweh, E Lughofer
Springer Science & Business Media, 2012
Handling drifts and shifts in on-line data streams with evolving fuzzy systems
E Lughofer, P Angelov
Applied Soft Computing 11 (2), 2057-2068, 2011
Extensions of vector quantization for incremental clustering
E Lughofer
Pattern recognition 41 (3), 995-1011, 2008
GENEFIS: Toward an effective localist network
M Pratama, SG Anavatti, E Lughofer
IEEE Transactions on Fuzzy Systems 22 (3), 547-562, 2013
Generalized smart evolving fuzzy systems
E Lughofer, C Cernuda, S Kindermann, M Pratama
Evolving systems 6 (4), 269-292, 2015
An incremental learning of concept drifts using evolving type-2 recurrent fuzzy neural networks
M Pratama, J Lu, E Lughofer, G Zhang, MJ Er
IEEE Transactions on Fuzzy Systems 25 (5), 1175-1192, 2016
On-line assurance of interpretability criteria in evolving fuzzy systems–achievements, new concepts and open issues
E Lughofer
Information sciences 251, 22-46, 2013
Single-pass active learning with conflict and ignorance
E Lughofer
Evolving Systems 3 (4), 251-271, 2012
pClass: an effective classifier for streaming examples
M Pratama, SG Anavatti, M Joo, ED Lughofer
IEEE Transactions on Fuzzy Systems 23 (2), 369-386, 2014
On-line elimination of local redundancies in evolving fuzzy systems
E Lughofer, JL Bouchot, A Shaker
Evolving systems 2, 165-187, 2011
Autonomous data stream clustering implementing split-and-merge concepts–towards a plug-and-play approach
E Lughofer, M Sayed-Mouchaweh
Information Sciences 304, 54-79, 2015
Hybrid active learning for reducing the annotation effort of operators in classification systems
E Lughofer
Pattern Recognition 45 (2), 884-896, 2012
Online active learning in data stream regression using uncertainty sampling based on evolving generalized fuzzy models
E Lughofer, M Pratama
IEEE Transactions on fuzzy systems 26 (1), 292-309, 2017
SparseFIS: Data-driven learning of fuzzy systems with sparsity constraints
E Lughofer, S Kindermann
IEEE Transactions on Fuzzy Systems 18 (2), 396-411, 2010
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