PUB - Publikationen an der Universität Bielefeld

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• [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. https://doi.org/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. https://doi.org/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. https://doi.org/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. In H. Yang, K. Pasupa, A. C. - S. Leung, J. T. Kwok, J. H. Chan, & I. King (Eds.), Lecture Notes in Computer Science: Vol. 12533. Neural Information Processing. 27th International Conference, ICONIP 2020, Bangkok, Thailand, November 23–27, 2020, Proceedings, Part II (pp. 738-749). Cham: Springer International Publishing. https://doi.org/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. In I. Farkaš, P. Masulli, & S. Wermter (Eds.), Lecture Notes in Computer Science: Vol. 12397. Artificial Neural Networks and Machine Learning – ICANN 2020. 29th International Conference on Artificial Neural Networks, Bratislava, Slovakia, September 15–18, 2020, Proceedings, Part II (pp. 204-213). Cham: Springer International Publishing. https://doi.org/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. In I. V. Tetko, V. Kůrková, P. Karpov, & F. Theis (Eds.), Lecture Notes in Computer Science. 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 (pp. 302-311). Cham: Springer International Publishing. https://doi.org/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), 9530-9536. IEEE. https://doi.org/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), 287-296. IEEE. https://doi.org/10.1109/ICDM.2018.00044

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• [42]
  

  2018 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2914730

  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. doi:10.1016/j.neucom.2017.06.084

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• [41]
  

  2017 | Konferenzbeitrag | PUB-ID: 2914734

  Losing, V., Hammer, B., & Wersing, H. (2017). Self-Adjusting Memory: How to Deal with Diverse Drift Types. Presented at the International Joint Conference on Artificial Intelligence (IJCAI) 2017, Melbourne. doi:10.24963/ijcai.2017/690

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• [40]
  

  2017 | Konferenzbeitrag | PUB-ID: 2914732

  Losing, V., Hammer, B., & Wersing, H. (2017). Personalized Maneuver Prediction at Intersections. Presented at the IEEE Intelligent Transportation Systems Conference 2017, Yokohama.

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• [39]
  

  2016 | Konferenzbeitrag | PUB-ID: 2907624

  Losing, V., Hammer, B., & Wersing, H. (2016). Choosing the Best Algorithm for an Incremental On-line Learning Task. Presented at the European Symposium on Artificial Neural Networks, Brügge.

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• [38]
  

  2016 | Konferenzbeitrag | PUB-ID: 2908455

  Losing, V., Hammer, B., & Wersing, H. (2016). 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.

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• [37]
  

  2016 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2907622

  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), 291-300. 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. doi:10.1016/j.neucom.2014.10.092

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• [35]
  

  2015 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2776021

  Losing, V., Hammer, B., & Wersing, H. (2015). Interactive Online Learning for Obstacle Classification on a Mobile Robot. Presented at the International Joint Conference on Neural Networks, Killarney, Ireland. 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 (Ed.), ESANN, 22nd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (pp. 41-46). 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. Presented at the International Conference on Pattern Recognition Applications and Methods, Vilamoura, Algarve, Portugal. https://doi.org/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. doi:10.1007/s12369-012-0145-z

  PUB | DOI | WoS
   
• [30]
  

  2012 | Report | PUB-ID: 2467027

  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, 1-6. https://doi.org/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 (Eds.), Workshop on CVPR for Human Communicative Behavior Analysis (pp. 79-85). San Francisco, California, USA: IEEE. https://doi.org/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, 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, 398-403. 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, 253-258. 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. https://doi.org/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), 45-53. https://doi.org/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. https://doi.org/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), 189-194. Toyama, Japan: IEEE. https://doi.org/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), 319-324

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• [18]
  

  2007 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1996923

  Wersing, H., Kirstein, S., Götting, M., Brandl, H., Dunn, M., Mikhailova, I., Görick, C., et al. (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) Bielefeld: Bielefeld University. https://doi.org/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., et al. (2007). Online Learning of Objects in a Biologically Motivated Visual Architecture. International Journal of Neural Systems, 17(04), 219-230. https://doi.org/10.1142/S0129065707001081

  PUB | DOI | WoS | PubMed | Europe PMC
   
• [16]
  

  2007 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1594761

  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. https://doi.org/10.1016/j.neucom.2006.11.020

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• [15]
  

  2006 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2142151

  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, 101-106.

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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. https://doi.org/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 (Ed.), 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., et al. (2006). A biologically motivated system for unconstrained online learning of visual objects. In S. Kollias (Ed.), Proc. of the Int. Conf. on Artificial Neural Networks (ICANN) (Vol. 2, pp. 508-517). Berlin, Heidelberg: Springer Berlin Heidelberg. https://doi.org/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.

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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, 48, 357-367. ELSEVIER SCIENCE BV. https://doi.org/10.1016/S0925-2312(01)00642-7

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• [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. https://doi.org/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. https://doi.org/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 (Ed.), Proc. of the 8th Europ. Symp. on Art. Neur. Netw. (ESANN)

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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), 1, 248-254

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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, 2, 739-744

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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. ICANN 99, Ninth Int. Conf. Artifical Neural Netwworks, Conference Publication / IEE, 470, 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, 295-300

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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 (Eds.), Int. Conf. on Artificial Neural Networks (pp. 439-444).

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