PUB - Publikationen an der Universität Bielefeld

Alle markieren

• [18]
  

  2021 | Bielefelder E-Dissertation | PUB-ID: 2956362

  Hosseini, B. (2021). Interpretable analysis of motion data. Bielefeld: Universität Bielefeld. https://doi.org/10.4119/unibi/2956362

  PUB | PDF | DOI
   
• [17]
  

  2019 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2982082

  Hosseini, B., & Hammer, B. (2019). Large-Margin Multiple Kernel Learning for Discriminative Features Selection and Representation Learning. 2019 International Joint Conference on Neural Networks (IJCNN), 1-8. IEEE. https://doi.org/10.1109/IJCNN.2019.8851982

  PUB | DOI
   
• [16]
  

  2019 | Konferenzbeitrag | Angenommen | PUB-ID: 2937842

  Hosseini, B., & Hammer, B. (Accepted). Deep-Aligned Convolutional Neural Network for Skeleton-based Action Recognition and Segmentation. Presented at the 2019 IEEE International Conference on Data Mining (ICDM), Beijing.

  PUB | Datei | arXiv
   
• [15]
  

  2019 | Konferenzbeitrag | Angenommen | PUB-ID: 2937841

  Hosseini, B., & Hammer, B. (Accepted). Interpretable Multiple-Kernel Prototype Learning for Discriminative Representation and Feature Selection. Presented at the The 28th ACM International Conference on Information and Knowledge Management (CIKM) , Beijing.

  PUB | Datei | arXiv
   
• [14]
  

  2019 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2937839

  Hosseini, B., & Hammer, B. (2019). Interpretable Discriminative Dimensionality Reduction and Feature Selection on the Manifold. Presented at the 2019 European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML), Würzburg.

  PUB | Datei | arXiv
   
• [13]
  

  2019 | Konferenzbeitrag | PUB-ID: 2930303

  Hosseini, B., & Hammer, B. (2019). Multiple-Kernel Dictionary Learning for Reconstruction and Clustering of Unseen Multivariate Time-series. In M. Verleysen (Ed.), Proceedings of the 27th European Symposium on Artificial Neural Networks (ESANN 2019)

  PUB | arXiv
   
• [12]
  

  2019 | Konferenzbeitrag | PUB-ID: 2934192

  Hosseini, B., & Hammer, B. (2019). Large-Margin Multiple Kernel Learning for Discriminative Features Selection and Representation Learning. Presented at the The 2019 International Joint Conference on Neural Networks (IJCNN), Budapest.

  PUB | arXiv
   
• [11]
  

  2018 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2982090

  Hosseini, B., & Hammer, B. (2018). Non-negative Local Sparse Coding for Subspace Clustering. In W. Duivesteijn, A. Siebes, & A. Ukkonen (Eds.), Lecture Notes in Computer Science. Advances in Intelligent Data Analysis XVII. 17th International Symposium, IDA 2018, ’s-Hertogenbosch, The Netherlands, October 24–26, 2018, Proceedings (pp. 137-150). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-030-01768-2_12

  PUB | DOI
   
• [10]
  

  2018 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2982087

  Hosseini, B., & Hammer, B. (2018). Confident Kernel Sparse Coding and Dictionary Learning. 2018 IEEE International Conference on Data Mining (ICDM), 1031-1036. IEEE. https://doi.org/10.1109/ICDM.2018.00130

  PUB | DOI
   
• [9]
  

  2018 | Preprint | Veröffentlicht | PUB-ID: 2921209

  Hosseini, B., & Hammer, B. (2018). Non-Negative Local Sparse Coding for Subspace Clustering. Advances in Intelligent Data Analysis XVII. IDA 2018

  PUB | Datei | Download (ext.) | arXiv
   
• [8]
  

  2018 | Konferenzbeitrag | Im Druck | PUB-ID: 2932116

  Hosseini, B., & Hammer, B. (In Press). Confident Kernel Sparse Coding and Dictionary Learning. 2018 IEEE International Conference on Data Mining (ICDM)

  PUB | Datei | arXiv
   
• [7]
  

  2018 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2919598

  Hosseini, B., & Hammer, B. (2018). Feasibility Based Large Margin Nearest Neighbor Metric Learning. ESANN 2018. Proceedings of 26th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, 219-224.

  PUB | arXiv
   
• [6]
  

  2017 | Kurzbeitrag Konferenz / Poster | PUB-ID: 2919987

  Hosseini, B., & Hammer, B. (2017). Non-negative Kernel Sparse Coding Frameworks for Efficient Analysis of Motion Data. Presented at the BMVA Symposium on Human Activity Recognition and Monitoring, London.

  PUB | PDF
   
• [5]
  

  2017 | Kurzbeitrag Konferenz / Poster | PUB-ID: 2919990

  Hosseini, B., & Hammer, B. (2017). Task-Driven Sparse Coding for Classification of Motion Data. Presented at the Ninth Mittweida Workshop on Computational Intelligence (MiWoCI 2017), Mittweida.

  PUB | PDF
   
• [4]
  

  2016 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2904469

  Hosseini, B., Hülsmann, F., Botsch, M., & Hammer, B. (2016). Non-Negative Kernel Sparse Coding for the Analysis of Motion Data. In A. E.P. Villa, P. Masulli, & A. Javier Pons Rivero (Eds.), Lecture Notes in Computer Science: Vol. 9887. Artificial Neural Networks and Machine Learning – ICANN 2016 (pp. 506-514). Cham: Springer. https://doi.org/10.1007/978-3-319-44781-0_60

  PUB | PDF | DOI | Download (ext.) | arXiv
   
• [3]
  

  2015 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2783165

  Hosseini, B., & Hammer, B. (2015). Efficient Metric Learning for the Analysis of Motion Data. 2015 IEEE International Conference on Data Science and Advanced Analytics (DSAA) Piscataway, NJ: IEEE. doi:10.1109/DSAA.2015.7344819

  PUB | DOI | Download (ext.) | arXiv
   
• [2]
  

  2010 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2914986

  Hosseini, B., Ahmadabadi, M. N., & Araabi, B. N. (2010). Abstract Concept Learning Approach Based on Behavioural Feature Extraction. In J. Kamaruzaman (Ed.), 2009 Second International Conference on Computer and Electrical Engineering (Vol. 2). Piscataway, NJ: IEEE. doi:10.1109/iccee.2009.223

  PUB | PDF | DOI
   
• [1]
  

  2008 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2914988

  Jamali, M. R., Arami, A., Hosseini, B., Moshiri, B., & Lukas, C. (2008). Real Time Emotional Control for Anti-Swing and Positioning Control of SIMO Overhead Traveling Crane. International Journal of Innovative Computing, Information, and Control, 4(9), 2333-2344.

  PUB | PDF