Frank-Michael Schleif

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Thomas VillmannProfessor for Mathematics and Computational Intelligence, University of Applied Sciences MittweidaE-mail confirmado em hs-mittweida.de
Barbara HammerProfessor, Bielefeld UniversityE-mail confirmado em techfak.uni-bielefeld.de
Michael BiehlUniversity of Groningen, Bernoulli InstituteE-mail confirmado em rug.nl
Christoph RaabUniversity of applied science Würzburg-SchweinfurtE-mail confirmado em fhws.de
Xibin ZhuCITEC, Center of Excellence, Bielefeld University, GermanyE-mail confirmado em techfak.uni-bielefeld.de
Peter TinoProfessor of Complex and Adaptive Systems, University of Birmingham, UKE-mail confirmado em bham.ac.uk
Kerstin BunteRosalind Franklin Fellow at the University of GroningenE-mail confirmado em rug.nl
Maximilian MünchUniversity of Groningen; University of Applied Sciences Würzburg-SchweinfurtE-mail confirmado em fhws.de
Tina GewenigerWHZ Westsächsische Hochschule ZwickauE-mail confirmado em fh-zwickau.de
Bassam MokbelCITEC, Bielefeld UniversityE-mail confirmado em techfak.uni-bielefeld.de
Philipp VaethTechnical University of Applied Sciences Würzburg-SchweinfurtE-mail confirmado em thws.de
Ernest Tonny MwebazeResearch ScientistE-mail confirmado em sunbird.ai
Simon HeiligELLIS PhD Student @ RUBE-mail confirmado em rub.de
Benjamin PaassenBielefeld UniversityE-mail confirmado em techfak.uni-bielefeld.de
Nicolai PetkovUniversity of GroningenE-mail confirmado em rug.nl
John QuinnGoogle Research, Sunbird AI, Makerere UniversityE-mail confirmado em cit.ac.ug
Mathias LindemannProfessor for Computer Software EngineeringE-mail confirmado em hs-bremerhaven.de
Peter MaassProfessor Mathematics, University of BremenE-mail confirmado em math.uni-bremen.de
Marika KadenHochschule MittweidaE-mail confirmado em hs-mittweida.de
marie cottrellSAMM Université Paris 1 Panthéon-SorbonneE-mail confirmado em univ-paris1.fr

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Frank-Michael Schleif

Professor of Computational Intelligence, Technical-UAS Würzburg-Schweinfurt

E-mail confirmado em thws.de - Página inicial

Machine learning computational intelligence streaming analysis kernel methods federated learning


Título Ordenar por citações Ordenar por ano Ordenar por título	Citado por Citado por	Ano
Reactive soft prototype computing for concept drift streams C Raab, M Heusinger, FM Schleif Neurocomputing 416, 340-351, 2020	183	2020
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	155	2012
Indefinite proximity learning: A review FM Schleif, P Tino Neural computation 27 (10), 2039-2096, 2015	111	2015
Learning vector quantization for (dis-) similarities B Hammer, D Hofmann, FM Schleif, X Zhu Neurocomputing 131, 43-51, 2014	91	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	77	2011
Metric and non-metric proximity transformations at linear costs A Gisbrecht, FM Schleif Neurocomputing 167, 643-657, 2015	62	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	58	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	53	2006
Efficient kernelized prototype based classification FM Schleif, T Villmann, B Hammer, P Schneider International journal of neural systems 21 (06), 443-457, 2011	45	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	40	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
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	36	2016
Margin-based active learning for LVQ networks FM Schleif, B Hammer, T Villmann Neurocomputing 70 (7-9), 1215-1224, 2007	34	2007
Prototype based fuzzy classification in clinical proteomics FM Schleif, T Villmann, B Hammer International Journal of Approximate Reasoning 47 (1), 4-16, 2008	30	2008
Indefinite core vector machine FM Schleif, P Tino Pattern Recognition 71, 187-195, 2017	28	2017
Data analysis of (non-) metric proximities at linear costs FM Schleif, A Gisbrecht International Workshop on Similarity-Based Pattern Recognition, 59-74, 2013	28	2013
Linear time relational prototype based learning A Gisbrecht, B Mokbel, FM Schleif, X Zhu, B Hammer International journal of neural systems 22 (05), 1250021, 2012	28	2012
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

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