Heiko Wersing

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

51 Publikationen

Alle markieren

  • [51]
    2024 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2993477
    Beierling, H., Richter, P., Brandt, M., Terfloth, L., Schulte, C., Wersing, H. & Vollmer, A.-L. (2024). What you need to know about a learning robot: Identifying the enabling architecture of complex systems. Cognitive Systems Research, 88: 101286. Elsevier . doi:10.1016/j.cogsys.2024.101286.
    PUB | DOI | Download (ext.) | WoS
     
  • [50]
    2021 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2959418
    Göpfert, J.P., Kuhl, U., Hindemith, L., Wersing, H. & Hammer, B. (2021). Intuitiveness in Active Teaching. IEEE Transactions on Human-Machine Systems, 1-10. Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/THMS.2021.3121666.
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  • [49]
    2020 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2950092
    Limberg, C., Wersing, H. & Ritter, H. (2020). Beyond Cross-Validation—Accuracy Estimation for Incremental and Active Learning Models. Machine Learning and Knowledge Extraction, 2(3), 327-346. MDPI AG. doi:10.3390/make2030018.
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  • [48]
    2020 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2950093
    Limberg, C., Wersing, H. & Ritter, H. (2020). Accuracy Estimation for an Incrementally Learning Cooperative Inventory Assistant Robot (Lecture Notes in Computer Science). In H. Yang, K. Pasupa, A.C.-S. Leung, J.T. Kwok, J.H. Chan & I. King (Hrsg.), Neural Information Processing. 27th International Conference, ICONIP 2020, Bangkok, Thailand, November 23–27, 2020, Proceedings, Part II (S. 738-749). Gehalten auf der 27th International Conference, ICONIP 2020, Cham: Springer International Publishing. doi:10.1007/978-3-030-63833-7_62.
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  • [47]
    2020 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2950094
    Limberg, C., Göpfert, J.P., Wersing, H. & Ritter, H. (2020). Prototype-Based Online Learning on Homogeneously Labeled Streaming Data (Lecture Notes in Computer Science). In I. Farkaš, P. Masulli & S. Wermter (Hrsg.), Artificial Neural Networks and Machine Learning – ICANN 2020. 29th International Conference on Artificial Neural Networks, Bratislava, Slovakia, September 15–18, 2020, Proceedings, Part II (S. 204-213). Gehalten auf der 29th International Conference on Artificial Neural Networks,, Cham: Springer International Publishing. doi:10.1007/978-3-030-61616-8_17.
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  • [46]
    2019 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2982085
    Göpfert, J.P., Wersing, H. & Hammer, B. (2019). Recovering Localized Adversarial Attacks (Lecture Notes in Computer Science). In I.V. Tetko, V. Kůrková, P. Karpov & F. Theis (Hrsg.), 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 (S. 302-311). Cham: Springer International Publishing. doi:10.1007/978-3-030-30487-4_24.
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  • [45]
    2019 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2982084
    Losing, V., Yoshikawa, T., Hasenjaeger, M., Hammer, B. & Wersing, H. (2019). Personalized Online Learning of Whole-Body Motion Classes using Multiple Inertial Measurement Units. 2019 International Conference on Robotics and Automation (ICRA) (S. 9530-9536). Gehalten auf der 2019 International Conference on Robotics and Automation (ICRA), IEEE. doi:10.1109/ICRA.2019.8794251.
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  • [44]
    2019 | Preprint | Veröffentlicht | PUB-ID: 2934181
    Göpfert, J.P., Wersing, H. & Hammer, B. (2019). Adversarial attacks hidden in plain sight. doi:10.4119/unibi/2934181.
    PUB | DOI | arXiv
     
  • [43]
    2018 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2982088
    Losing, V., Wersing, H. & Hammer, B. (2018). Enhancing Very Fast Decision Trees with Local Split-Time Predictions. 2018 IEEE International Conference on Data Mining (ICDM) (S. 287-296). Gehalten auf der 2018 IEEE International Conference on Data Mining (ICDM), IEEE. doi:10.1109/ICDM.2018.00044.
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  • [42]
    2018 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2914730 OA
    Losing, V., Hammer, B. & Wersing, H. (2018). Incremental on-line learning: A review and comparison of state of the art algorithms. Neurocomputing, 275, 1261-1274. Elsevier BV. doi:10.1016/j.neucom.2017.06.084.
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  • [41]
    2017 | Konferenzbeitrag | PUB-ID: 2914734 OA
    Losing, V., Hammer, B. & Wersing, H. (2017). Self-Adjusting Memory: How to Deal with Diverse Drift Types. Gehalten auf der International Joint Conference on Artificial Intelligence (IJCAI) 2017, International Joint Conferences on Artificial Intelligence. doi:10.24963/ijcai.2017/690.
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  • [40]
    2017 | Konferenzbeitrag | PUB-ID: 2914732 OA
    Losing, V., Hammer, B. & Wersing, H. (2017). Personalized Maneuver Prediction at Intersections. Gehalten auf der IEEE Intelligent Transportation Systems Conference 2017.
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  • [39]
    2016 | Konferenzbeitrag | PUB-ID: 2907624 OA
    Losing, V., Hammer, B. & Wersing, H. (2016). Choosing the Best Algorithm for an Incremental On-line Learning Task. Gehalten auf der European Symposium on Artificial Neural Networks.
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  • [38]
    2016 | Konferenzbeitrag | PUB-ID: 2908455 OA
    Losing, V., Hammer, B. & Wersing, H. (2016). Dedicated Memory Models for Continual Learning in the Presence of Concept Drift. Gehalten auf der Continual Learning Workshop of the Thirtieth Annual Conference on Neural Information Processing Systems (NIPS).
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  • [37]
    2016 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2907622 OA
    Losing, V., Hammer, B. & Wersing, H. (2016). KNN Classifier with Self Adjusting Memory for Heterogeneous Concept Drift. 2016 IEEE 16th International Conference on Data Mining (ICDM) (S. 291-300). Gehalten auf der International Conference On Data Mining, Piscataway, NJ: IEEE. doi:10.1109/ICDM.2016.0040.
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  • [36]
    2015 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2772413
    Fischer, L., Hammer, B. & Wersing, H. (2015). Efficient rejection strategies for prototype-based classification. Neurocomputing, 169(SI), 334-342. Elsevier. doi:10.1016/j.neucom.2014.10.092.
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  • [35]
    2015 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2776021 OA
    Losing, V., Hammer, B. & Wersing, H. (2015). Interactive Online Learning for Obstacle Classification on a Mobile Robot. Gehalten auf der International Joint Conference on Neural Networks, IEEE. doi:10.1109/IJCNN.2015.7280610.
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  • [34]
    2014 | Patent | PUB-ID: 2906899
    Wersing, H. & Queißer, J. (01.06.2016) System for Controlling an Automated Device.
    PUB | Download (ext.)
     
  • [33]
    2014 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2673548
    Fischer, L., Hammer, B. & Wersing, H. (2014). Rejection strategies for learning vector quantization. In M. Verleysen (Hrsg.), ESANN, 22nd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (S. 41-46). Gehalten auf der ESANN, Bruges, Belgium: i6doc.com.
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  • [32]
    2012 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2425561
    Dornbusch, D., Haschke, R., Menzel, S. & Wersing, H. (2012). Decomposition of Multimodal Data for Affordance-based Identification of Potential Grasps (S. 585-589). Gehalten auf der International Conference on Pattern Recognition Applications and Methods, SciTePress. doi:10.5220/0003790205850589.
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  • [31]
    2012 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2604349
    Lang, C., Wachsmuth, S., Hanheide, M. & Wersing, H. (2012). Facial Communicative Signals - Valence Recognition in Task-Oriented Human-Robot Interaction. International Journal of Social Robotics - Special Issue on Measuring Human-Robot Interaction, 4(3), 249-262. Springer Science + Business Media. doi:10.1007/s12369-012-0145-z.
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  • [30]
    2012 | Report | PUB-ID: 2467027 OA
    Lang, C., Wachsmuth, S., Hanheide, M. & Wersing, H. (2012). Facial Communicative Signal Interpretation in Human-Robot Interaction by Discriminative Video Subsequence Selection.
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  • [29]
    2010 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2376395
    John, S., Wersing, H. & Ritter, H. (2010). An iterative approach to local-PCA. Neural Networks (IJCNN), The 2010 International Joint Conference on (S. 1-6). Gehalten auf der IJCNN. doi:10.1109/IJCNN.2010.5596615.
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  • [28]
    2010 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2034704
    Lang, C., Wachsmuth, S., Wersing, H. & Hanheide, M. (2010). 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 (Hrsg.), Workshop on CVPR for Human Communicative Behavior Analysis (S. 79-85). San Francisco, California, USA: IEEE. doi:10.1109/CVPRW.2010.5543264.
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  • [27]
    2010 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2034035
    Denecke, A., Ayllon Clemente, I., Wersing, H., Eggert, J. & Steil, J.J. (2010). Figure-ground segmentation using metrics adaptation in levelset methods. European Symposium on Artificial Neural Networks (S. 417-422).
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  • [26]
    2010 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2019022
    Dornbusch, D., Haschke, R., Menzel, S. & Wersing, H. (2010). Correlating shape and functional properties using decomposition approaches. Proc. FLAIRS-23 (S. 398-403). Gehalten auf der FLAIRS-23, AAAI.
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  • [25]
    2010 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2019017
    Dornbusch, D., Haschke, R., Menzel, S. & Wersing, H. (2010). Finding Correlations in Multimodal Data Using Decomposition Approaches. Proc. ESANN 2010 (S. 253-258). Gehalten auf der ESANN 2010, Belgium: d-facto.
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  • [24]
    2009 | Patent | PUB-ID: 2637645
    Wersing, H., Götting, M. & Steil, J.J. (2009). Adaptive scene dependent filters in online learning environments.
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  • [23]
    2009 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1969815
    Denecke, A., Wersing, H., Steil, J.J. & Körner, E. (2009). Online figure-ground segmentation with adaptive metrics in Generalized LVQ. Neurocomputing, 72(7-9), 1470-1482. Elsevier BV. doi:10.1016/j.neucom.2008.11.028.
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  • [22]
    2009 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1969825
    Denecke, A., Wersing, H., Steil, J.J. & Körner, E. (2009). Incremental Figure-Ground Segmentation using localized adaptive metrics in LVQ. International Workshop on Self-Organizing Maps (WSOM) (S. 45-53). Gehalten auf der 7th International Workshop on Self-Organizing Maps (WSOM). doi:10.1007/978-3-642-02397-2_6.
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  • [21]
    2009 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1969830
    Kirstein, S., Denecke, A., Hasler, S., Wersing, H., Gross, H.-M. & Körner, E. (2009). A Vision Architecture for Unconstrained and Incremental Learning of Multiple Categories. Memetic Computing, 1(4), 291-304. Springer Science + Business Media. doi:10.1007/s12293-009-0023-x.
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  • [20]
    2009 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1991920
    Lang, C., Hanheide, M., Lohse, M., Wersing, H. & Sagerer, G. (2009). Feedback Interpretation based on Facial Expressions in Human–Robot Interaction. International Symposium on Robot and Human Interactive Communication (RO-MAN'09) (S. 189-194). Gehalten auf der 18th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN’09), Toyama, Japan: IEEE. doi:10.1109/ROMAN.2009.5326199.
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  • [19]
    2008 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2142096
    Denecke, A., Wersing, H., Steil, J.J. & Körner, E. (2008). Robust object segmentation by adaptive metrics in Generalized LVQ. Proc. of the European Symposium on Artificial Neural Networks (ESANN) (S. 319-324).
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  • [18]
    2007 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1996923 OA
    Wersing, H., Kirstein, S., Götting, M., Brandl, H., Dunn, M., Mikhailova, I., Görick, C., Steil, J.J., Ritter, H. & Körner, E. (2007). Online Learning of Objects and Faces in an Integrated Biologically Motivated Architecture. Proceedings of the 5th International Conference on Computer Vision Systems (ICVS 2007) . Gehalten auf der 5th International Conference on Computer Vision Systems (ICVS), Bielefeld: Bielefeld University. doi:10.2390/biecoll-icvs2007-67.
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  • [17]
    2007 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1632803
    Wersing, H., Kirstein, S., Götting, M., Brandl, H., Dunn, M., Mikhailova, I., Görick, C., Steil, J.J., Ritter, H. & Körner, E. (2007). Online Learning of Objects in a Biologically Motivated Visual Architecture. International Journal of Neural Systems, 17(04), 219-230. WORLD SCIENTIFIC PUBL CO PTE LTD. doi:10.1142/S0129065707001081.
    PUB | DOI | WoS | PubMed | Europe PMC
     
  • [16]
    2007 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1594761 OA
    Steil, J.J., Götting, M., Wersing, H., Körner, E. & Ritter, H. (2007). Adaptive scene dependent filters for segmentation and online learning of visual objects. Neurocomputing, 70(7-9), 1235-1246. Elsevier Science Ltd. doi:10.1016/j.neucom.2006.11.020.
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  • [15]
    2006 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2142151 OA
    Götting, M., Steil, J.J., Wersing, H., Körner, E. & Ritter, H. (2006). Adaptive scene-dependent filters in online learning environments. New issues in neurocomputing. 13th European Symposium on Artificial Neural Networks 2005 (S. 101-106). Gehalten auf der 13th European Symposium on Artificial Neural Networks.
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  • [14]
    2006 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2142182
    Weng, S., Wersing, H., Steil, J.J. & Ritter, H. (2006). Learning Lateral Interactions for Feature Binding and Sensory Segmentation from Prototypic Basis Interactions. IEEE Trans. Neural Networks, 17(4), 843-862. Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/TNN.2006.873295.
    PUB | DOI | WoS | PubMed | Europe PMC
     
  • [13]
    2006 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2142157
    Steil, J.J. & Wersing, H. (2006). Recent Trends in Online Learning for Cognitive Robotics. In M. Verleysen (Hrsg.), Proc. European Symposium on Artifical Neural Networks. d-side publications.
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  • [12]
    2006 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2142171
    Wersing, H., Kirstein, S., Götting, M., Brandl, H., Dunn, M., Mikhailova, I., Görick, C., Steil, J.J., Ritter, H. & Körner, E. (2006). A biologically motivated system for unconstrained online learning of visual objects. In S. Kollias (Hrsg.), Proc. of the Int. Conf. on Artificial Neural Networks (ICANN) (S. 508-517). Berlin, Heidelberg: Springer Berlin Heidelberg. doi:10.1007/11840930_53.
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  • [11]
    2004 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2714352
    Ontrup, J., Wersing, H. & Ritter, H. (2004). A Computational Feature Binding Model of Human Texture Perception. Cognitive Processing, 5(1), 32-44. Springer Science + Business Media.
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  • [10]
    2002 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1613503
    Nattkemper, T.W., Wersing, H., Schubert, W. & Ritter, H. (2002). A neural network architecture for automatic segmentation of fluorescence micrographs (NEUROCOMPUTING). Neurocomputing (S. 357-367). ELSEVIER SCIENCE BV. doi:10.1016/S0925-2312(01)00642-7.
    PUB | DOI | WoS
     
  • [9]
    2001 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1616820
    Wersing, H., Beyn, W.-J. & Ritter, H. (2001). Dynamical stability conditions for recurrent neural networks with unsaturating piecewise linear transfer functions. Neural Computation, 13(8), 1811-1825. M I T PRESS. doi:10.1162/08997660152469350.
    PUB | DOI | WoS | PubMed | Europe PMC
     
  • [8]
    2001 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2142293
    Wersing, H., Steil, J.J. & Ritter, H. (2001). A Competitive Layer Model for Feature Binding and Sensory Segmentation. Neural Computation, 13(2), 357-387. MIT Press - Journals. doi:10.1162/089976601300014574.
    PUB | DOI | WoS | PubMed | Europe PMC
     
  • [7]
    2000 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2375211
    Nattkemper, T.W., Wersing, H., Schubert, W. & Ritter, H. (2000). A Neural Network Architecture for Automatic Segmentation of Fluorescence Micrographs. In M. Verleysen (Hrsg.), Proc. of the 8th Europ. Symp. on Art. Neur. Netw. (ESANN). Gehalten auf der 8th European Symposium on Artificial Neural Networks.
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  • [6]
    2000 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2374799
    Nattkemper, T.W., Wersing, H., Schubert, W. & Ritter, H. (2000). Fluorescence Micrograph Segmentation by Gestalt-Based Feature Binding. Proc. of the Int. Joint Conf. on Neur. Netw. (IJCNN) (S. 248-254). Como, Italy.
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  • [5]
    2000 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2714744
    Nattkemper, T.W., Wersing, H., Schubert, W. & Ritter, H. (2000). 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 (S. 739-744). Gehalten auf der International Conference on Mathematics and Engineering Techniques in Medicine and Biological Sciences, Las Vegas, USA.
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  • [4]
    2000 | Dissertation | PUB-ID: 2434189
    Wersing, H. (2000). Spatial feature binding and learning in competitive neural layer architectures. Göttingen: Cuvillier.
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  • [3]
    1999 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2714834
    Wersing, H. & Ritter, H. (1999). Backtracking Deterministic Annealing for Constraint Satisfaction Problems (Conference Publication / IEE). ICANN 99, Ninth Int. Conf. Artifical Neural Netwworks (S. 868-875). London: IEE.
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  • [2]
    1999 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2714852
    Wersing, H. & Ritter, H. (1999). Feature Binding and Relaxation Labeling with the Competitive Layer Model. Proceedings. ESANN '99, 7th European Symposium on Artificial Neural Networks (S. 295-300). Gehalten auf der ESANN '99, Bruges.
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  • [1]
    1997 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2142325
    Wersing, H., Steil, J.J. & Ritter, H. (1997). A Layered Recurrent Neural Network for Feature Grouping. In W. Gerstner, A. Germond, M. Hasler & J.-D. Nicoud (Hrsg.), Int. Conf. on Artificial Neural Networks (S. 439-444).
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