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 | 656 | 2017 |
Evolving fuzzy systems-methodologies, advanced concepts and applications E Lughofer Springer, 2011 | 487 | 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 | 375 | 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 | 292 | 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 | 278 | 2013 |
Evolving fuzzy classifiers using different model architectures P Angelov, E Lughofer, X Zhou Fuzzy sets and systems 159 (23), 3160-3182, 2008 | 227 | 2008 |
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 | 200 | 2011 |
Extensions of vector quantization for incremental clustering E Lughofer Pattern recognition 41 (3), 995-1011, 2008 | 196 | 2008 |
Learning in non-stationary environments: methods and applications M Sayed-Mouchaweh, E Lughofer Springer Science & Business Media, 2012 | 193 | 2012 |
GENEFIS: Toward an effective localist network M Pratama, SG Anavatti, E Lughofer IEEE Transactions on Fuzzy Systems 22 (3), 547-562, 2013 | 185 | 2013 |
Generalized smart evolving fuzzy systems E Lughofer, C Cernuda, S Kindermann, M Pratama Evolving systems 6 (4), 269-292, 2015 | 177 | 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 | 175 | 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 | 142 | 2013 |
Single-pass active learning with conflict and ignorance E Lughofer Evolving Systems 3 (4), 251-271, 2012 | 133 | 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 | 130 | 2014 |
On-line elimination of local redundancies in evolving fuzzy systems E Lughofer, JL Bouchot, A Shaker Evolving systems 2, 165-187, 2011 | 127 | 2011 |
Hybrid active learning for reducing the annotation effort of operators in classification systems E Lughofer Pattern Recognition 45 (2), 884-896, 2012 | 123 | 2012 |
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 | 120 | 2015 |
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 | 117 | 2017 |
Predictive maintenance in dynamic systems: advanced methods, decision support tools and real-world applications E Lughofer, M Sayed-Mouchaweh Springer, 2019 | 116 | 2019 |