50 Publikationen
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2020 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2950093Limberg C, Wersing H, Ritter H. Accuracy Estimation for an Incrementally Learning Cooperative Inventory Assistant Robot. In: Yang H, Pasupa K, Leung AC-S, Kwok JT, Chan JH, King I, eds. Neural Information Processing. 27th International Conference, ICONIP 2020, Bangkok, Thailand, November 23–27, 2020, Proceedings, Part II. Lecture Notes in Computer Science. Vol 12533. Cham: Springer International Publishing; 2020: 738-749.PUB | DOI
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2020 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2950094Limberg C, Göpfert JP, Wersing H, Ritter H. Prototype-Based Online Learning on Homogeneously Labeled Streaming Data. In: Farkaš I, Masulli P, Wermter S, eds. Artificial Neural Networks and Machine Learning – ICANN 2020. 29th International Conference on Artificial Neural Networks, Bratislava, Slovakia, September 15–18, 2020, Proceedings, Part II. Lecture Notes in Computer Science. Vol 12397. Cham: Springer International Publishing; 2020: 204-213.PUB | DOI
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2019 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2982085Göpfert JP, Wersing H, Hammer B. Recovering Localized Adversarial Attacks. In: Tetko IV, Kůrková V, Karpov P, Theis F, eds. Artificial Neural Networks and Machine Learning – ICANN 2019: Theoretical Neural Computation. 28th International Conference on Artificial Neural Networks, Munich, Germany, September 17–19, 2019, Proceedings, Part I. Lecture Notes in Computer Science. Cham: Springer International Publishing; 2019: 302-311.PUB | DOI
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2019 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2982084Losing V, Yoshikawa T, Hasenjaeger M, Hammer B, Wersing H. Personalized Online Learning of Whole-Body Motion Classes using Multiple Inertial Measurement Units. In: 2019 International Conference on Robotics and Automation (ICRA). IEEE; 2019: 9530-9536.PUB | DOI
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2016 | Konferenzbeitrag | PUB-ID: 2908455Losing V, Hammer B, Wersing H. Dedicated Memory Models for Continual Learning in the Presence of Concept Drift. Presented at the Continual Learning Workshop of the Thirtieth Annual Conference on Neural Information Processing Systems (NIPS), Barcelona.PUB | PDF
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2014 | Patent | PUB-ID: 2906899Wersing H, Queißer J. System for Controlling an Automated Device. 01.06.2016.PUB | Download (ext.)
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2014 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2673548Fischer L, Hammer B, Wersing H. Rejection strategies for learning vector quantization. In: Verleysen M, ed. ESANN, 22nd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Bruges, Belgium: i6doc.com; 2014: 41-46.PUB
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2012 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2425561Dornbusch D, Haschke R, Menzel S, Wersing H. Decomposition of Multimodal Data for Affordance-based Identification of Potential Grasps. Presented at the International Conference on Pattern Recognition Applications and Methods, Vilamoura, Algarve, Portugal.PUB | DOI
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2012 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2604349Lang C, Wachsmuth S, Hanheide M, Wersing H. Facial Communicative Signals - Valence Recognition in Task-Oriented Human-Robot Interaction. International Journal of Social Robotics - Special Issue on Measuring Human-Robot Interaction. 2012;4(3):249-262.PUB | DOI | WoS
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2010 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2034704Lang C, Wachsmuth S, Wersing H, Hanheide M. Facial Expressions as Feedback Cue in Human-Robot Interaction - a Comparison between Human and Automatic Recognition Performances. In: IEEE Computer Society, Institute of Electrical and Electronics Engineers, eds. Workshop on CVPR for Human Communicative Behavior Analysis. San Francisco, California, USA: IEEE; 2010: 79-85.PUB | DOI
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2010 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2034035Denecke A, Ayllon Clemente I, Wersing H, Eggert J, Steil JJ. Figure-ground segmentation using metrics adaptation in levelset methods. In: European Symposium on Artificial Neural Networks. 2010: 417-422.PUB
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2010 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2019022Dornbusch D, Haschke R, Menzel S, Wersing H. Correlating shape and functional properties using decomposition approaches. In: Proc. FLAIRS-23. AAAI; 2010: 398-403.PUB
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2010 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2019017Dornbusch D, Haschke R, Menzel S, Wersing H. Finding Correlations in Multimodal Data Using Decomposition Approaches. In: Proc. ESANN 2010. Belgium: d-facto; 2010: 253-258.PUB
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2009 | Patent | PUB-ID: 2637645Wersing H, Götting M, Steil JJ. Adaptive scene dependent filters in online learning environments. 2009.PUB
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2009 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1991920Lang C, Hanheide M, Lohse M, Wersing H, Sagerer G. Feedback Interpretation based on Facial Expressions in Human–Robot Interaction. In: International Symposium on Robot and Human Interactive Communication (RO-MAN'09). Toyama, Japan: IEEE; 2009: 189-194.PUB | DOI
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2008 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2142096Denecke A, Wersing H, Steil JJ, Körner E. Robust object segmentation by adaptive metrics in Generalized LVQ. In: Proc. of the European Symposium on Artificial Neural Networks (ESANN). 2008: 319-324.PUB
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2007 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1996923Wersing H, Kirstein S, Götting M, et al. Online Learning of Objects and Faces in an Integrated Biologically Motivated Architecture. In: Proceedings of the 5th International Conference on Computer Vision Systems (ICVS 2007) . Bielefeld: Bielefeld University; 2007.PUB | PDF | DOI
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2007 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1632803Wersing H, Kirstein S, Götting M, et al. Online Learning of Objects in a Biologically Motivated Visual Architecture. International Journal of Neural Systems. 2007;17(04):219-230.PUB | DOI | WoS | PubMed | Europe PMC
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2006 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2142182Weng S, Wersing H, Steil JJ, Ritter H. Learning Lateral Interactions for Feature Binding and Sensory Segmentation from Prototypic Basis Interactions. IEEE Trans. Neural Networks. 2006;17(4):843-862.PUB | DOI | WoS | PubMed | Europe PMC
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2006 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2142157Steil JJ, Wersing H. Recent Trends in Online Learning for Cognitive Robotics. In: Verleysen M, ed. Proc. European Symposium on Artifical Neural Networks. d-side publications; 2006.PUB
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2006 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2142171Wersing H, Kirstein S, Götting M, et al. A biologically motivated system for unconstrained online learning of visual objects. In: Kollias S, ed. Proc. of the Int. Conf. on Artificial Neural Networks (ICANN). Vol 2. Berlin, Heidelberg: Springer Berlin Heidelberg; 2006: 508-517.PUB | DOI
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2004 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2714352Ontrup J, Wersing H, Ritter H. A Computational Feature Binding Model of Human Texture Perception. Cognitive Processing. 2004;5(1):32-44.PUB
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2001 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1616820Wersing H, Beyn W-J, Ritter H. Dynamical stability conditions for recurrent neural networks with unsaturating piecewise linear transfer functions. Neural Computation. 2001;13(8):1811-1825.PUB | DOI | WoS | PubMed | Europe PMC
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2001 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2142293Wersing H, Steil JJ, Ritter H. A Competitive Layer Model for Feature Binding and Sensory Segmentation. Neural Computation. 2001;13(2):357-387.PUB | DOI | WoS | PubMed | Europe PMC
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2000 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2375211Nattkemper TW, Wersing H, Schubert W, Ritter H. A Neural Network Architecture for Automatic Segmentation of Fluorescence Micrographs. In: Verleysen M, ed. Proc. of the 8th Europ. Symp. on Art. Neur. Netw. (ESANN). 2000.PUB
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2000 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2374799Nattkemper TW, Wersing H, Schubert W, Ritter H. Fluorescence Micrograph Segmentation by Gestalt-Based Feature Binding. In: Proc. of the Int. Joint Conf. on Neur. Netw. (IJCNN). Vol 1. Como, Italy; 2000: 248-254.PUB
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2000 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2714744Nattkemper TW, Wersing H, Schubert W, Ritter H. Automatic Evaluation of Multi-Parameter Fluorescence Micrographs with a Neural Network Architecture. In: Proceedings of the International Conference on Mathematics and Engineering Techniques in Medicine and Biological Sciences, METMBS '00. Vol 2. Las Vegas, USA; 2000: 739-744.PUB
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2000 | Dissertation | PUB-ID: 2434189Wersing H. Spatial feature binding and learning in competitive neural layer architectures. Göttingen: Cuvillier; 2000.PUB
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1999 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2714834Wersing H, Ritter H. Backtracking Deterministic Annealing for Constraint Satisfaction Problems. In: ICANN 99, Ninth Int. Conf. Artifical Neural Netwworks. Conference Publication / IEE. Vol 470. London: IEE; 1999: 868-875.PUB
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1999 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2714852Wersing H, Ritter H. Feature Binding and Relaxation Labeling with the Competitive Layer Model. In: Proceedings. ESANN '99, 7th European Symposium on Artificial Neural Networks. Bruges; 1999: 295-300.PUB
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1997 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2142325Wersing H, Steil JJ, Ritter H. A Layered Recurrent Neural Network for Feature Grouping. In: Gerstner W, Germond A, Hasler M, Nicoud J-D, eds. Int. Conf. on Artificial Neural Networks. 1997: 439-444.PUB