Daniel Kottke

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Bernhard SickProfessor of Intelligent Embedded Systems, University of KasselDirección de correo verificada de uni-kassel.de
Georg KremplInformation and Computing Sciences, Utrecht University, The NetherlandsDirección de correo verificada de uu.nl
Denis HuseljicUniversity of KasselDirección de correo verificada de uni-kassel.de
Marek HerdeUniversity of KasselDirección de correo verificada de uni-kassel.de
Myra SpiliopoulouProfessor in Computer Science, Otto-von-Guericke University MagdeburgDirección de correo verificada de ovgu.de
Christoph SandrockUniversität KasselDirección de correo verificada de uni-kassel.de
Pham Minh TuanUniversität KasselDirección de correo verificada de uni-kassel.de
Michael HankeINM-7, Research Center Jülich and Inst. Sys. Neurosci., Medical Faculty, HHU Düsseldorf, GermanyDirección de correo verificada de fz-juelich.de
Vincent LemaireOrange LabsDirección de correo verificada de orange.com
Sven TomfordeUniversität Kiel, Intelligent SystemsDirección de correo verificada de informatik.uni-kiel.de
Maarten BieshaarRobert Bosch GmbHDirección de correo verificada de de.bosch.com
Klemens BöhmUniv.-Prof. Dr.-Ing., IPD - Institute of Program Structures and Data OrganizationDirección de correo verificada de kit.edu
Holger TrittenbachExBDirección de correo verificada de exb.de
Adrian EnglhardtAI Engineer @ LinkedIn | Previously: Researcher, Karlsruhe Institute of TechnologyDirección de correo verificada de kit.edu
George KachergisResearch Scientist, Arizona State UniversityDirección de correo verificada de stanford.edu
Ali SunyaevTechnical University of Munich (TUM)Dirección de correo verificada de tum.de
Christian GruhlUniversity of KasselDirección de correo verificada de uni-kassel.de
Edwin LughoferJohannes Kepler University LinzDirección de correo verificada de jku.at
Gino GulamhusseneOtto-von-Guericke-University MagdeburgDirección de correo verificada de ovgu.de
Adrian Calma

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Daniel Kottke

Researcher, Kassel University

Dirección de correo verificada de uni-kassel.de - Página principal

Active Learning Data Mining Machine Learning Pattern Recognition


Título Ordenar por citas Ordenar por año Ordenar por título	Citado por Citado por	Año
A studyforrest extension, simultaneous fMRI and eye gaze recordings during prolonged natural stimulation M Hanke, N Adelhöfer, D Kottke, V Iacovella, A Sengupta, FR Kaule, ... Scientific data 3 (1), 1-15, 2016	120	2016
Optimised probabilistic active learning (OPAL) for fast, non-myopic, cost-sensitive active classification G Krempl, D Kottke, V Lemaire Machine Learning 100, 449-476, 2015	56	2015
Transfer learning for time series anomaly detection V Vercruyssen, W Meert, J Davis Proceedings of the Workshop and Tutorial on Interactive Adaptive Learning …, 2017	45	2017
Challenges of Reliable, Realistic and Comparable Active Learning Evaluation D Kottke, A Calma, D Huseljic, G Krempl, B Sick Proceedings of the Workshop and Tutorial on Interactive Adaptive Learning …, 2017	38	2017
Probabilistic active learning in datastreams D Kottke, G Krempl, M Spiliopoulou Advances in Intelligent Data Analysis XIV: 14th International Symposium, IDA …, 2015	34	2015
Multi-Class Probabilistic Active Learning D Kottke, G Krempl, D Lang, J Teschner, M Spiliopoulou Frontiers in Artificial Intelligence and Applications 285, 586-594 (ECAI), 2016	31	2016
Separation of aleatoric and epistemic uncertainty in deterministic deep neural networks D Huseljic, B Sick, M Herde, D Kottke 2020 25th International Conference on Pattern Recognition (ICPR), 9172-9179, 2021	30	2021
Learning to Learn: Dynamic Runtime Exploitation of Various Knowledge Sources and Machine Learning Paradigms A Calma, D Kottke, B Sick, S Tomforde 2nd IEEE International Workshops on Foundations and Applications of Self …, 2017	29	2017
A comparative study on hyperparameter optimization for recommender systems P Matuszyk, RT Castillo, D Kottke, M Spiliopoulou Workshop on Recommender Systems and Big Data Analytics (RS-BDA'16).-2016.-Р …, 2016	29	2016
Toward optimal probabilistic active learning using a Bayesian approach D Kottke, M Herde, C Sandrock, D Huseljic, G Krempl, B Sick Machine learning 110 (6), 1199-1231, 2021	28	2021
Stream-based active learning for sliding windows under the influence of verification latency T Pham, D Kottke, G Krempl, B Sick Machine Learning, 1-26, 2022	25	2022
Probabilistic active learning: Towards combining versatility, optimality and efficiency G Krempl, D Kottke, M Spiliopoulou Discovery Science: 17th International Conference, DS 2014, Bled, Slovenia …, 2014	20	2014
Limitations of assessing active learning performance at runtime D Kottke, J Schellinger, D Huseljic, B Sick arXiv preprint arXiv:1901.10338, 2019	17	2019
Probabilistic Active Learning for Active Class Selection D Kottke, G Krempl, M Stecklina, CS von Rekowski, T Sabsch, TP Minh, ... Future of Interactive Learning Machines Workshop @NIPS 2016, 2016	16*	2016
scikit-activeml: A library and toolbox for active learning algorithms D Kottke, M Herde, A Benz, P Mergard, A Roghman, C Sandrock, B Sick	11	2021
A studyforrest extension, simultaneous fMRI and eye gaze recordings during prolonged natural stimulation. Scientific Data, 3, 160092 M Hanke, N Adelhöfer, D Kottke, V Iacovella, A Sengupta, FR Kaule, ...	10	2016
Active sorting–an efficient training of a sorting robot with active learning techniques M Herde, D Kottke, A Calma, M Bieshaar, S Deist, B Sick 2018 international joint conference on neural networks (IJCNN), 1-8, 2018	9	2018
Multi-annotator probabilistic active learning M Herde, D Kottke, D Huseljic, B Sick 2020 25th International Conference on Pattern Recognition (ICPR), 10281-10288, 2021	8	2021
Active learning with realistic data-a case study A Calma, M Stolz, D Kottke, S Tomforde, B Sick 2018 International Joint Conference on Neural Networks (IJCNN), 1-8, 2018	7	2018
Probabilistic active learning: A short proposition G Krempl, D Kottke, M Spiliopoulou ECAI 2014, 1049-1050, 2014	7	2014

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