Frank-Michael Schleif

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Thomas VillmannProfessor for Mathematics and Computational Intelligence, University of Applied Sciences MittweidaEmail verificata su hs-mittweida.de
Barbara HammerProfessor, Bielefeld UniversityEmail verificata su techfak.uni-bielefeld.de
Michael BiehlUniversity of Groningen, Bernoulli InstituteEmail verificata su rug.nl
Christoph RaabUniversity of applied science Würzburg-SchweinfurtEmail verificata su fhws.de
Xibin ZhuCITEC, Center of Excellence, Bielefeld University, GermanyEmail verificata su techfak.uni-bielefeld.de
Peter TinoProfessor of Complex and Adaptive Systems, University of Birmingham, UKEmail verificata su cs.bham.ac.uk
Kerstin BunteRosalind Franklin Fellow at the University of GroningenEmail verificata su rug.nl
Maximilian MünchUniversity of Groningen; University of Applied Sciences Würzburg-SchweinfurtEmail verificata su fhws.de
Tina GewenigerWHZ Westsächsische Hochschule ZwickauEmail verificata su fh-zwickau.de
Bassam MokbelCITEC, Bielefeld UniversityEmail verificata su techfak.uni-bielefeld.de
Philipp VaethTechnical University of Applied Sciences Würzburg-SchweinfurtEmail verificata su thws.de
Ernest Tonny MwebazeResearch ScientistEmail verificata su sunbird.ai
Nicolai PetkovUniversity of GroningenEmail verificata su rug.nl
Simon HeiligELLIS PhD Student @ RUBEmail verificata su rub.de
Benjamin PaassenBielefeld UniversityEmail verificata su techfak.uni-bielefeld.de
John QuinnGoogle Research, Sunbird AI, Makerere UniversityEmail verificata su cit.ac.ug
Javier MonroyUniversity of MalagaEmail verificata su uma.es
Javier Gonzalez-JimenezProfessor, University of MalagaEmail verificata su uma.es
Jose Luis Blanco ClaracoFull Professor (Profesor Catedrático de Universidad), University of AlmeríaEmail verificata su ual.es
Mathias LindemannProfessor for Computer Software EngineeringEmail verificata su hs-bremerhaven.de

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Frank-Michael Schleif

Professor of Computational Intelligence, Technical-UAS Würzburg-Schweinfurt

Email verificata su thws.de - Home page

Machine learning computational intelligence streaming analysis kernel methods federated learning


Titolo Ordina per citazioni Ordina per anno Ordina per titolo	Citata da Citata da	Anno
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	153	2012
Reactive soft prototype computing for concept drift streams C Raab, M Heusinger, FM Schleif Neurocomputing 416, 340-351, 2020	150	2020
Indefinite proximity learning: A review FM Schleif, P Tino Neural computation 27 (10), 2039-2096, 2015	107	2015
Learning vector quantization for (dis-) similarities B Hammer, D Hofmann, FM Schleif, X Zhu Neurocomputing 131, 43-51, 2014	87	2014
Divergence-based classification in learning vector quantization E Mwebaze, P Schneider, FM Schleif, JR Aduwo, JA Quinn, S Haase, ... Neurocomputing 74 (9), 1429-1435, 2011	76	2011
Metric and non-metric proximity transformations at linear costs A Gisbrecht, FM Schleif Neurocomputing 167, 643-657, 2015	59	2015
Classification of mass-spectrometric data in clinical proteomics using learning vector quantization methods T Villmann, FM Schleif, M Kostrzewa, A Walch, B Hammer Briefings in Bioinformatics 9 (2), 129-143, 2008	57	2008
Fuzzy classification by fuzzy labeled neural gas T Villmann, B Hammer, F Schleif, T Geweniger, W Herrmann Neural Networks 19 (6-7), 772-779, 2006	52	2006
Efficient kernelized prototype based classification FM Schleif, T Villmann, B Hammer, P Schneider International journal of neural systems 21 (06), 443-457, 2011	44	2011
Comparison of relevance learning vector quantization with other metric adaptive classification methods T Villmann, F Schleif, B Hammer Neural Networks 19 (5), 610-622, 2006	39	2006
Stationarity of matrix relevance LVQ M Biehl, B Hammer, FM Schleif, P Schneider, T Villmann 2015 International Joint Conference on Neural Networks (IJCNN), 1-8, 2015	38	2015
Metric learning for sequences in relational LVQ B Mokbel, B Paassen, FM Schleif, B Hammer Neurocomputing 169, 306-322, 2015	37	2015
Margin-based active learning for LVQ networks FM Schleif, B Hammer, T Villmann Neurocomputing 70 (7-9), 1215-1224, 2007	34	2007
Odor recognition in robotics applications by discriminative time-series modeling FM Schleif, B Hammer, JG Monroy, JG Jimenez, JL Blanco-Claraco, ... Pattern Analysis and Applications 19, 207-220, 2016	33	2016
Prototype based fuzzy classification in clinical proteomics FM Schleif, T Villmann, B Hammer International Journal of Approximate Reasoning 47 (1), 4-16, 2008	31	2008
Indefinite core vector machine FM Schleif, P Tino Pattern Recognition 71, 187-195, 2017	28	2017
Large margin linear discriminative visualization by matrix relevance learning M Biehl, K Bunte, FM Schleif, P Schneider, T Villmann The 2012 International Joint Conference on Neural Networks (IJCNN), 1-8, 2012	28	2012
Learning interpretable kernelized prototype-based models D Hofmann, FM Schleif, B Paaßen, B Hammer Neurocomputing 141, 84-96, 2014	27	2014
Support vector classification of proteomic profile spectra based on feature extraction with the bi-orthogonal discrete wavelet transform FM Schleif, M Lindemann, M Diaz, P Maaß, J Decker, T Elssner, M Kuhn, ... Computing and Visualization in Science 12, 189-199, 2009	27	2009
Cancer informatics by prototype networks in mass spectrometry FM Schleif, T Villmann, M Kostrzewa, B Hammer, A Gammerman Artificial Intelligence in Medicine 45 (2-3), 215-228, 2009	27	2009

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