Heiko Wersing

heiko.wersing@honda-ri.de
PEVZ-ID
54878447

51 Publikationen

Alle markieren

  • [51]
    2024 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2993477
    H. Beierling, et al., “What you need to know about a learning robot: Identifying the enabling architecture of complex systems”, Cognitive Systems Research, vol. 88, 2024, : 101286.
    PUB | DOI | Download (ext.) | WoS
     
  • [50]
    2021 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2959418
    J.P. Göpfert, et al., “Intuitiveness in Active Teaching”, IEEE Transactions on Human-Machine Systems, 2021, pp. 1-10.
    PUB | DOI | WoS
     
  • [49]
    2020 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2950092
    C. Limberg, H. Wersing, and H. Ritter, “Beyond Cross-Validation—Accuracy Estimation for Incremental and Active Learning Models”, Machine Learning and Knowledge Extraction, vol. 2, 2020, pp. 327-346.
    PUB | DOI | WoS
     
  • [48]
    2020 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2950093
    C. Limberg, H. Wersing, and H. Ritter, “Accuracy Estimation for an Incrementally Learning Cooperative Inventory Assistant Robot”, Neural Information Processing. 27th International Conference, ICONIP 2020, Bangkok, Thailand, November 23–27, 2020, Proceedings, Part II, H. Yang, et al., eds., Lecture Notes in Computer Science, vol. 12533, Cham: Springer International Publishing, 2020, pp.738-749.
    PUB | DOI
     
  • [47]
    2020 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2950094
    C. Limberg, et al., “Prototype-Based Online Learning on Homogeneously Labeled Streaming Data”, Artificial Neural Networks and Machine Learning – ICANN 2020. 29th International Conference on Artificial Neural Networks, Bratislava, Slovakia, September 15–18, 2020, Proceedings, Part II, I. Farkaš, P. Masulli, and S. Wermter, eds., Lecture Notes in Computer Science, vol. 12397, Cham: Springer International Publishing, 2020, pp.204-213.
    PUB | DOI
     
  • [46]
    2019 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2982085
    J.P. Göpfert, H. Wersing, and B. Hammer, “Recovering Localized Adversarial Attacks”, 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, I.V. Tetko, et al., eds., Lecture Notes in Computer Science, Cham: Springer International Publishing, 2019, pp.302-311.
    PUB | DOI
     
  • [45]
    2019 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2982084
    V. Losing, et al., “Personalized Online Learning of Whole-Body Motion Classes using Multiple Inertial Measurement Units”, 2019 International Conference on Robotics and Automation (ICRA), IEEE, 2019, pp.9530-9536.
    PUB | DOI
     
  • [44]
    2019 | Preprint | Veröffentlicht | PUB-ID: 2934181
    J.P. Göpfert, H. Wersing, and B. Hammer, “Adversarial attacks hidden in plain sight”, 2019.
    PUB | DOI | arXiv
     
  • [43]
    2018 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2982088
    V. Losing, H. Wersing, and B. Hammer, “Enhancing Very Fast Decision Trees with Local Split-Time Predictions”, 2018 IEEE International Conference on Data Mining (ICDM), IEEE, 2018, pp.287-296.
    PUB | DOI
     
  • [42]
    2018 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2914730 OA
    V. Losing, B. Hammer, and H. Wersing, “Incremental on-line learning: A review and comparison of state of the art algorithms”, Neurocomputing, vol. 275, 2018, pp. 1261-1274.
    PUB | PDF | DOI | WoS
     
  • [41]
    2017 | Konferenzbeitrag | PUB-ID: 2914734 OA
    V. Losing, B. Hammer, and H. Wersing, “Self-Adjusting Memory: How to Deal with Diverse Drift Types”, Presented at the International Joint Conference on Artificial Intelligence (IJCAI) 2017, Melbourne, International Joint Conferences on Artificial Intelligence, 2017.
    PUB | PDF | DOI
     
  • [40]
    2017 | Konferenzbeitrag | PUB-ID: 2914732 OA
    V. Losing, B. Hammer, and H. Wersing, “Personalized Maneuver Prediction at Intersections”, Presented at the IEEE Intelligent Transportation Systems Conference 2017, Yokohama, 2017.
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  • [39]
    2016 | Konferenzbeitrag | PUB-ID: 2907624 OA
    V. Losing, B. Hammer, and H. Wersing, “Choosing the Best Algorithm for an Incremental On-line Learning Task”, Presented at the European Symposium on Artificial Neural Networks, Brügge, 2016.
    PUB | PDF
     
  • [38]
    2016 | Konferenzbeitrag | PUB-ID: 2908455 OA
    V. Losing, B. Hammer, and H. Wersing, “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, 2016.
    PUB | PDF
     
  • [37]
    2016 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2907622 OA
    V. Losing, B. Hammer, and H. Wersing, “KNN Classifier with Self Adjusting Memory for Heterogeneous Concept Drift”, 2016 IEEE 16th International Conference on Data Mining (ICDM), Piscataway, NJ: IEEE, 2016, pp.291-300.
    PUB | PDF | DOI
     
  • [36]
    2015 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2772413
    L. Fischer, B. Hammer, and H. Wersing, “Efficient rejection strategies for prototype-based classification”, Neurocomputing, vol. 169, 2015, pp. 334-342.
    PUB | DOI | WoS
     
  • [35]
    2015 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2776021 OA
    V. Losing, B. Hammer, and H. Wersing, “Interactive Online Learning for Obstacle Classification on a Mobile Robot”, Presented at the International Joint Conference on Neural Networks, Killarney, Ireland, IEEE, 2015.
    PUB | PDF | DOI
     
  • [34]
    2014 | Patent | PUB-ID: 2906899
    H. Wersing and J. Queißer, “System for Controlling an Automated Device”, 01.06.2016.
    PUB | Download (ext.)
     
  • [33]
    2014 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2673548
    L. Fischer, B. Hammer, and H. Wersing, “Rejection strategies for learning vector quantization”, ESANN, 22nd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, M. Verleysen, ed., Bruges, Belgium: i6doc.com, 2014, pp.41-46.
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  • [32]
    2012 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2425561
    D. Dornbusch, et al., “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, SciTePress, 2012.
    PUB | DOI
     
  • [31]
    2012 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2604349
    C. Lang, et al., “Facial Communicative Signals - Valence Recognition in Task-Oriented Human-Robot Interaction”, International Journal of Social Robotics - Special Issue on Measuring Human-Robot Interaction, vol. 4, 2012, pp. 249-262.
    PUB | DOI | WoS
     
  • [30]
    2012 | Report | PUB-ID: 2467027 OA
    C. Lang, et al., Facial Communicative Signal Interpretation in Human-Robot Interaction by Discriminative Video Subsequence Selection, 2012.
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  • [29]
    2010 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2376395
    S. John, H. Wersing, and H. Ritter, “An iterative approach to local-PCA”, Neural Networks (IJCNN), The 2010 International Joint Conference on, 2010, pp.1-6.
    PUB | DOI
     
  • [28]
    2010 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2034704
    C. Lang, et al., “Facial Expressions as Feedback Cue in Human-Robot Interaction - a Comparison between Human and Automatic Recognition Performances”, Workshop on CVPR for Human Communicative Behavior Analysis, IEEE Computer Society and Institute of Electrical and Electronics Engineers, eds., San Francisco, California, USA: IEEE, 2010, pp.79-85.
    PUB | DOI
     
  • [27]
    2010 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2034035
    A. Denecke, et al., “Figure-ground segmentation using metrics adaptation in levelset methods”, European Symposium on Artificial Neural Networks, 2010, pp.417-422.
    PUB
     
  • [26]
    2010 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2019022
    D. Dornbusch, et al., “Correlating shape and functional properties using decomposition approaches”, Proc. FLAIRS-23, AAAI, 2010, pp.398-403.
    PUB
     
  • [25]
    2010 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2019017
    D. Dornbusch, et al., “Finding Correlations in Multimodal Data Using Decomposition Approaches”, Proc. ESANN 2010, Belgium: d-facto, 2010, pp.253-258.
    PUB
     
  • [24]
    2009 | Patent | PUB-ID: 2637645
    H. Wersing, M. Götting, and J.J. Steil, “Adaptive scene dependent filters in online learning environments”, 2009.
    PUB
     
  • [23]
    2009 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1969815
    A. Denecke, et al., “Online figure-ground segmentation with adaptive metrics in Generalized LVQ”, Neurocomputing, vol. 72, 2009, pp. 1470-1482.
    PUB | DOI | WoS
     
  • [22]
    2009 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1969825
    A. Denecke, et al., “Incremental Figure-Ground Segmentation using localized adaptive metrics in LVQ”, International Workshop on Self-Organizing Maps (WSOM), 2009, pp.45-53.
    PUB | DOI
     
  • [21]
    2009 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1969830
    S. Kirstein, et al., “A Vision Architecture for Unconstrained and Incremental Learning of Multiple Categories”, Memetic Computing, vol. 1, 2009, pp. 291-304.
    PUB | DOI
     
  • [20]
    2009 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1991920
    C. Lang, et al., “Feedback Interpretation based on Facial Expressions in Human–Robot Interaction”, International Symposium on Robot and Human Interactive Communication (RO-MAN'09), Toyama, Japan: IEEE, 2009, pp.189-194.
    PUB | DOI
     
  • [19]
    2008 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2142096
    A. Denecke, et al., “Robust object segmentation by adaptive metrics in Generalized LVQ”, Proc. of the European Symposium on Artificial Neural Networks (ESANN), 2008, pp.319-324.
    PUB
     
  • [18]
    2007 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1996923 OA
    H. Wersing, et al., “Online Learning of Objects and Faces in an Integrated Biologically Motivated Architecture”, Proceedings of the 5th International Conference on Computer Vision Systems (ICVS 2007) , Bielefeld: Bielefeld University, 2007.
    PUB | PDF | DOI
     
  • [17]
    2007 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1632803
    H. Wersing, et al., “Online Learning of Objects in a Biologically Motivated Visual Architecture”, International Journal of Neural Systems, vol. 17, 2007, pp. 219-230.
    PUB | DOI | WoS | PubMed | Europe PMC
     
  • [16]
    2007 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1594761 OA
    J.J. Steil, et al., “Adaptive scene dependent filters for segmentation and online learning of visual objects”, Neurocomputing, vol. 70, 2007, pp. 1235-1246.
    PUB | PDF | DOI | WoS
     
  • [15]
    2006 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2142151 OA
    M. Götting, et al., “Adaptive scene-dependent filters in online learning environments”, New issues in neurocomputing. 13th European Symposium on Artificial Neural Networks 2005, 2006, pp.101-106.
    PUB | PDF
     
  • [14]
    2006 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2142182
    S. Weng, et al., “Learning Lateral Interactions for Feature Binding and Sensory Segmentation from Prototypic Basis Interactions”, IEEE Trans. Neural Networks, vol. 17, 2006, pp. 843-862.
    PUB | DOI | WoS | PubMed | Europe PMC
     
  • [13]
    2006 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2142157
    J.J. Steil and H. Wersing, “Recent Trends in Online Learning for Cognitive Robotics”, Proc. European Symposium on Artifical Neural Networks, M. Verleysen, ed., d-side publications, 2006.
    PUB
     
  • [12]
    2006 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2142171
    H. Wersing, et al., “A biologically motivated system for unconstrained online learning of visual objects”, Proc. of the Int. Conf. on Artificial Neural Networks (ICANN), S. Kollias, ed., vol. 2, Berlin, Heidelberg: Springer Berlin Heidelberg, 2006, pp.508-517.
    PUB | DOI
     
  • [11]
    2004 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2714352
    J. Ontrup, H. Wersing, and H. Ritter, “A Computational Feature Binding Model of Human Texture Perception”, Cognitive Processing, vol. 5, 2004, pp. 32-44.
    PUB
     
  • [10]
    2002 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1613503
    T.W. Nattkemper, et al., “A neural network architecture for automatic segmentation of fluorescence micrographs”, Neurocomputing, NEUROCOMPUTING, vol. 48, ELSEVIER SCIENCE BV, 2002, pp.357-367.
    PUB | DOI | WoS
     
  • [9]
    2001 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1616820
    H. Wersing, W.-J. Beyn, and H. Ritter, “Dynamical stability conditions for recurrent neural networks with unsaturating piecewise linear transfer functions”, Neural Computation, vol. 13, 2001, pp. 1811-1825.
    PUB | DOI | WoS | PubMed | Europe PMC
     
  • [8]
    2001 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2142293
    H. Wersing, J.J. Steil, and H. Ritter, “A Competitive Layer Model for Feature Binding and Sensory Segmentation”, Neural Computation, vol. 13, 2001, pp. 357-387.
    PUB | DOI | WoS | PubMed | Europe PMC
     
  • [7]
    2000 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2375211
    T.W. Nattkemper, et al., “A Neural Network Architecture for Automatic Segmentation of Fluorescence Micrographs”, Proc. of the 8th Europ. Symp. on Art. Neur. Netw. (ESANN), M. Verleysen, ed., 2000.
    PUB
     
  • [6]
    2000 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2374799
    T.W. Nattkemper, et al., “Fluorescence Micrograph Segmentation by Gestalt-Based Feature Binding”, Proc. of the Int. Joint Conf. on Neur. Netw. (IJCNN), vol. 1, Como, Italy: 2000, pp.248-254.
    PUB
     
  • [5]
    2000 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2714744
    T.W. Nattkemper, et al., “Automatic Evaluation of Multi-Parameter Fluorescence Micrographs with a Neural Network Architecture”, Proceedings of the International Conference on Mathematics and Engineering Techniques in Medicine and Biological Sciences, METMBS '00, vol. 2, Las Vegas, USA: 2000, pp.739-744.
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  • [4]
    2000 | Dissertation | PUB-ID: 2434189
    H. Wersing, Spatial feature binding and learning in competitive neural layer architectures, Göttingen: Cuvillier, 2000.
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  • [3]
    1999 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2714834
    H. Wersing and H. Ritter, “Backtracking Deterministic Annealing for Constraint Satisfaction Problems”, ICANN 99, Ninth Int. Conf. Artifical Neural Netwworks, Conference Publication / IEE, vol. 470, London: IEE, 1999, pp.868-875.
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  • [2]
    1999 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2714852
    H. Wersing and H. Ritter, “Feature Binding and Relaxation Labeling with the Competitive Layer Model”, Proceedings. ESANN '99, 7th European Symposium on Artificial Neural Networks, Bruges: 1999, pp.295-300.
    PUB
     
  • [1]
    1997 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2142325
    H. Wersing, J.J. Steil, and H. Ritter, “A Layered Recurrent Neural Network for Feature Grouping”, Int. Conf. on Artificial Neural Networks, W. Gerstner, et al., eds., 1997, pp.439-444.
    PUB
     
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