Barbara Hammer

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Thomas VillmannProfessor for Mathematics and Computational Intelligence, University of Applied Sciences MittweidaVerified email at hs-mittweida.de
Michael BiehlUniversity of Groningen, Bernoulli InstituteVerified email at rug.nl
Frank-Michael SchleifProfessor of Computational Intelligence, Technical-UAS Würzburg-SchweinfurtVerified email at thws.de
Alexander SchulzTechnical Faculty, CITEC, Bielefeld UniversityVerified email at techfak.uni-bielefeld.de
Bassam MokbelCITEC, Bielefeld UniversityVerified email at techfak.uni-bielefeld.de
Heiko WersingHonda Research Institute EuropeVerified email at honda-ri.de
Benjamin PaassenBielefeld UniversityVerified email at techfak.uni-bielefeld.de
Xibin ZhuCITEC, Center of Excellence, Bielefeld University, GermanyVerified email at techfak.uni-bielefeld.de
Kerstin BunteRosalind Franklin Fellow at the University of GroningenVerified email at rug.nl
Viktor LosingCrowdStrike, ParisVerified email at crowdstrike.com
Alessandro SperdutiUniversity of PadovaVerified email at math.unipd.it
Alessio MicheliDepartment of Computer Science, University of PisaVerified email at di.unipi.it
Niels PinkwartHumboldt-Universität zu Berlin & DFKIVerified email at informatik.hu-berlin.de
Christina GöpfertAssistant Researcher at Bielefeld UniversityVerified email at techfak.uni-bielefeld.de
Jan Philip GöpfertBielefeld UniversityVerified email at uni-bielefeld.de
Peter TinoProfessor of Complex and Adaptive Systems, University of Birmingham, UKVerified email at cs.bham.ac.uk
Babak HosseiniPostdoctoral Researcher in Pattern Recognition in Embedded Systems, TU DortmundVerified email at techfak.uni-bielefeld.de
marie cottrellSAMM Université Paris 1 Panthéon-SorbonneVerified email at univ-paris1.fr
David NebelCI Group MittweidaVerified email at hs-mittweida.de
Pascal HitzlerLloyd T. Smith Creativity in Engineering Chair, Kansas State University, Manhattan, KansasVerified email at ksu.edu

Barbara Hammer

Professor, Bielefeld University

Verified email at techfak.uni-bielefeld.de

machine learning data mining neural networks bioinformatics theoretical computer science


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
Generalized relevance learning vector quantization B Hammer, T Villmann Neural Networks 15 (8-9), 1059-1068, 2002	565	2002
Adaptive relevance matrices in learning vector quantization P Schneider, M Biehl, B Hammer Neural computation 21 (12), 3532-3561, 2009	435	2009
Incremental learning algorithms and applications A Geppert, B Hammer ESANN, 2016	407	2016
Incremental on-line learning: A review and comparison of state of the art algorithms V Losing, B Hammer, H Wersing Neurocomputing 275, 1261-1274, 2018	347	2018
KNN Classifier with Self Adjusting Memory for Heterogeneous Concept Drift V Losing, B Hammer, H Wersing IEEE International Conference on Data Mining (ICDM), 2016	247	2016
Parametric nonlinear dimensionality reduction using kernel t-SNE A Gisbrecht, A Schulz, B Hammer Neurocomputing 147, 71-82, 2015	244	2015
Neural maps in remote sensing image analysis T Villmann, E Merényi, B Hammer Neural Networks 16 (3-4), 389-403, 2003	238	2003
Batch and median neural gas M Cottrell, B Hammer, A Hasenfuß, T Villmann Neural Networks 19 (6-7), 762-771, 2006	197	2006
Supervised neural gas with general similarity measure B Hammer, M Strickert, T Villmann Neural Processing Letters 21, 21-44, 2005	192	2005
Merge SOM for temporal data M Strickert, B Hammer Neurocomputing 64, 39-71, 2005	176	2005
Recursive self-organizing network models B Hammer, A Micheli, A Sperduti, M Strickert Neural Networks 17 (8-9), 1061-1085, 2004	167	2004
On the approximation capability of recurrent neural networks B Hammer Neurocomputing 31 (1-4), 107-123, 2000	166	2000
Prototype‐based models in machine learning M Biehl, B Hammer, T Villmann Wiley Interdisciplinary Reviews: Cognitive Science 7 (2), 92-111, 2016	154	2016
A note on the universal approximation capability of support vector machines B Hammer, K Gersmann neural processing letters 17, 43-53, 2003	147	2003
Limited rank matrix learning, discriminative dimension reduction and visualization K Bunte, P Schneider, B Hammer, FM Schleif, T Villmann, M Biehl Neural Networks 26, 159-173, 2012	142	2012
Dynamics and Generalization Ability of LVQ Algorithms. M Biehl, A Ghosh, B Hammer Journal of Machine Learning Research 8 (2), 2007	140	2007
Distance learning in discriminative vector quantization P Schneider, M Biehl, B Hammer Neural computation 21 (10), 2942-2969, 2009	138	2009
A general framework for dimensionality-reducing data visualization mapping K Bunte, M Biehl, B Hammer Neural Computation 24 (3), 771-804, 2012	129	2012
A general framework for unsupervised processing of structured data B Hammer, A Micheli, A Sperduti, M Strickert Neurocomputing 57, 3-35, 2004	129	2004
Perspectives of neural-symbolic integration B Hammer, P Hitzler Springer, 2007	119	2007

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