52 Publikationen

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  • [52]
    2024 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2987573
    Grimmelsmann, N., Mechtenberg, M., Vieth, M., Schulz, A., Hammer, B., & Schneider, A. (2024). Predicting the Level of Co-Activation of One Muscle Head from the Other Muscle Head of the Biceps Brachii Muscle by Linear Regression and Shallow Feedforward Neural Networks. Proceedings of the 17th International Joint Conference on Biomedical Engineering Systems and Technologies, 611-621. SCITEPRESS - Science and Technology Publications. https://doi.org/10.5220/0012368700003657
    PUB | DOI
     
  • [51]
    2024 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2987572
    Schroeder, S., Schulz, A., Hinder, F., & Hammer, B. (2024). Semantic Properties of Cosine Based Bias Scores for Word Embeddings. Proceedings of the 13th International Conference on Pattern Recognition Applications and Methods, 160-168. SCITEPRESS - Science and Technology Publications. https://doi.org/10.5220/0012577200003654
    PUB | DOI
     
  • [50]
    2023 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2977923 OA
    Nelkner, J., Huang, L., Lin, T. W., Schulz, A., Osterholz, B., Henke, C., Blom, J., et al. (2023). Abundance, classification and genetic potential of Thaumarchaeota in metagenomes of European agricultural soils: a meta-analysis. Environmental Microbiome, 18(1), 26. https://doi.org/10.1186/s40793-023-00479-9
    PUB | PDF | DOI | WoS | PubMed | Europe PMC
     
  • [49]
    2023 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2985684
    Kummert, J., Schulz, A., Feldhans, R., Habigt, M., Stemmler, M., Kohler, C., Abel, D., et al. (2023). Generating Cardiovascular Data to Improve Training of Assistive Heart Devices. 2023 IEEE Symposium Series on Computational Intelligence (SSCI), 1292-1297. IEEE. https://doi.org/10.1109/SSCI52147.2023.10372030
    PUB | DOI
     
  • [48]
    2023 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2985683
    Feldhans, R., Schulz, A., Kummert, J., Habigt, M., Stemmler, M., Kohler, C., Abel, D., et al. (2023). Data Augmentation for Cardiovascular Time Series Data Using WaveNet. 2023 IEEE Symposium Series on Computational Intelligence (SSCI), 836-841. IEEE. https://doi.org/10.1109/SSCI52147.2023.10371813
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  • [47]
    2023 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2983457
    Schroeder, S., Schulz, A., Tarakanov, I., Feldhans, R., & Hammer, B. (2023). Measuring Fairness with Biased Data: A Case Study on the Effects of Unsupervised Data in Fairness Evaluation. In I. Rojas, G. Joya, & A. Catala (Eds.), Lecture Notes in Computer Science. Advances in Computational Intelligence. 17th International Work-Conference on Artificial Neural Networks, IWANN 2023, Ponta Delgada, Portugal, June 19–21, 2023, Proceedings, Part I (pp. 134-145). Cham: Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-43085-5_11
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  • [46]
    2023 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2983455
    Liuliakov, A., Schulz, A., Hermes, L., & Hammer, B. (2023). One-Class Intrusion Detection with Dynamic Graphs. In L. Iliadis, A. Papaleonidas, P. Angelov, & C. Jayne (Eds.), Lecture Notes in Computer Science. Artificial Neural Networks and Machine Learning – ICANN 2023. 32nd International Conference on Artificial Neural Networks, Heraklion, Crete, Greece, September 26–29, 2023, Proceedings, Part IV (pp. 537-549). Cham: Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-44216-2_44
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  • [45]
    2023 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2983250
    Vieth, M., Schulz, A., & Hammer, B. (2023). Extending Drift Detection Methods to Identify When Exactly the Change Happened. In I. Rojas, G. Joya, & A. Catala (Eds.), Lecture Notes in Computer Science. Advances in Computational Intelligence. 17th International Work-Conference on Artificial Neural Networks, IWANN 2023, Ponta Delgada, Portugal, June 19–21, 2023, Proceedings, Part I (pp. 92-104). Cham: Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-43085-5_8
    PUB | DOI
     
  • [44]
    2023 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2980429
    Kummert, J., Schulz, A., & Hammer, B. (2023). Metric Learning with Self-Adjusting Memory for Explaining Feature Drift. SN Computer Science, 4(4), 376. https://doi.org/10.1007/s42979-023-01782-5
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  • [43]
    2023 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2969383
    Artelt, A., Schulz, A., & Hammer, B. (2023). "Why Here and not There?": Diverse Contrasting Explanations of Dimensionality Reduction. Proceedings of the 12th International Conference on Pattern Recognition Applications and Methods, 27-38. Setúbal, Portugal: SCITEPRESS - Science and Technology Publications. https://doi.org/10.5220/0011618300003411
    PUB | DOI | arXiv
     
  • [42]
    2023 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2969381
    Schroeder, S., Schulz, A., Kenneweg, P., & Hammer, B. (2023). So Can We Use Intrinsic Bias Measures or Not? Proceedings of the 12th International Conference on Pattern Recognition Applications and Methods, 403-410. Setúbal, Portugal: SCITEPRESS - Science and Technology Publications. https://doi.org/10.5220/0011693700003411
    PUB | DOI
     
  • [41]
    2023 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2969382
    Kenneweg, P., Schroeder, S., Schulz, A., & Hammer, B. (2023). Debiasing Sentence Embedders Through Contrastive Word Pairs. Proceedings of the 12th International Conference on Pattern Recognition Applications and Methods, 205-212. Setúbal, Portugal: SCITEPRESS - Science and Technology Publications. https://doi.org/10.5220/0011615300003411
    PUB | DOI
     
  • [40]
    2022 | Zeitschriftenaufsatz | PUB-ID: 2978998
    Paaßen, B., Schulz, A., C. Stewart, T., & Hammer, B. (2022). Reservoir Memory Machines as Neural Computers. IEEE Transactions on Neural Networks and Learning Systems, 33(6), 2575–2585. https://doi.org/10.1109/TNNLS.2021.3094139
    PUB | DOI | Download (ext.) | arXiv
     
  • [39]
    2022 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2967096
    Kenneweg, P., Schulz, A., Schroeder, S., & Hammer, B. (2022). Intelligent Learning Rate Distribution to Reduce Catastrophic Forgetting in Transformers. In H. Yin, D. Camacho, & P. Tino (Eds.), Lecture Notes in Computer Science: Vol. 13756. Intelligent Data Engineering and Automated Learning – IDEAL 2022. 23rd International Conference, IDEAL 2022, Manchester, UK, November 24–26, 2022, Proceedings (pp. 252-261). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-031-21753-1_25
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  • [38]
    2022 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2964829
    Langnickel, L., Schulz, A., Hammer, B., & Fluck, J. (2022). BERT WEAVER: Using WEight AVERaging to Enable Lifelong Learning for Transformer-based Models. arXiv. https://doi.org/10.48550/ARXIV.2202.10101
    PUB | DOI | arXiv
     
  • [37]
    2021 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2954542
    Paaßen, B., Schulz, A., & Hammer, B. (2021). Reservoir Stack Machines. Neurocomputing, 470, 352-364. https://doi.org/10.1016/j.neucom.2021.05.106
    PUB | DOI | Download (ext.) | WoS | arXiv
     
  • [36]
    2021 | Zeitschriftenaufsatz | E-Veröff. vor dem Druck | PUB-ID: 2956229
    Paassen, B., Schulz, A., Stewart, T. C., & Hammer, B. (2021). Reservoir Memory Machines as Neural Computers. IEEE Transactions on Neural Networks and Learning Systems, 1-11. https://doi.org/10.1109/TNNLS.2021.3094139
    PUB | DOI | Download (ext.) | WoS | PubMed | Europe PMC | arXiv
     
  • [35]
    2021 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2952937 OA
    Kummert, J., Schulz, A., Redick, T., Ayoub, N., Modabber, A., Abel, D., & Hammer, B. (2021). Efficient Reject Options for Particle Filter Object Tracking in Medical Applications. Sensors, 21(6), 2114. https://doi.org/10.3390/s21062114
    PUB | PDF | DOI | WoS | PubMed | Europe PMC
     
  • [34]
    2020 | Konferenzbeitrag | PUB-ID: 2943260
    Schulz, A., Hinder, F., & Hammer, B. (2020). DeepView: Visualizing Classification Boundaries of Deep Neural Networks as Scatter Plots Using Discriminative Dimensionality Reduction. Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, {IJCAI-20}. https://doi.org/10.24963/ijcai.2020/319
    PUB | DOI | Download (ext.) | arXiv
     
  • [33]
    2020 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2941931
    Paaßen, B., & Schulz, A. (2020). Reservoir memory machines. In M. Verleysen (Ed.), Proceedings of the 28th European Symposium on Artificial Neural Networks (ESANN 2020) (pp. 567-572). Bruges: i6doc.
    PUB | Download (ext.) | arXiv
     
  • [32]
    2020 | Kurzbeitrag Konferenz / Poster | Veröffentlicht | PUB-ID: 2952742
    Panda, A., Yadav, A., Yeerna, H., Singh, A., Biehl, M., Lux, M., Schulz, A., et al. (2020). The composition of the human ribosome varies significantly in different normal and malignant tissues. Proceedings: AACR Annual Meeting 2020, Cancer Research, 80 Philadelphia: Amer Assoc Cancer Research. https://doi.org/10.1158/1538-7445.AM2020-5865
    PUB | DOI | WoS
     
  • [31]
    2020 | Zeitschriftenaufsatz | E-Veröff. vor dem Druck | PUB-ID: 2944327
    Panda, A., Yadav, A., Yeerna, H., Singh, A., Biehl, M., Lux, M., Schulz, A., et al. (2020). Tissue- and development-stage-specific mRNA and heterogeneous CNV signatures of human ribosomal proteins in normal and cancer samples. Nucleic acids research. doi:10.1093/nar/gkaa485
    PUB | DOI | WoS | PubMed | Europe PMC
     
  • [30]
    2019 | Zeitschriftenaufsatz | E-Veröff. vor dem Druck | PUB-ID: 2934458 OA
    Prahm, C., Schulz, A., Paaßen, B., Schoisswohl, J., Kaniusas, E., Dorffner, G., Hammer, B., et al. (2019). Counteracting Electrode Shifts in Upper-Limb Prosthesis Control via Transfer Learning. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 27(5), 956-962. doi:10.1109/TNSRE.2019.2907200
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  • [29]
    2018 | Konferenzbeitrag | PUB-ID: 2930001 OA
    Schulz, A., Queißer, J., Ishihara, H., & Asada, M. (2018). Transfer Learning of Complex Motor Skills on the Humanoid Robot Affetto. Presented at the International Conference on Development and Learning and on Epigenetic Robotics 2018 (ICDL-EPIROB2018), Tokyo (In Press).
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  • [28]
    2018 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2914505
    Paaßen, B., Schulz, A., Hahne, J., & Hammer, B. (2018). Expectation maximization transfer learning and its application for bionic hand prostheses. Neurocomputing, 298, 122-133. doi:10.1016/j.neucom.2017.11.072
    PUB | DOI | Download (ext.) | WoS | arXiv
     
  • [27]
    2018 | Konferenzbeitrag | PUB-ID: 2916318
    Berger, K., Schulz, A., Paaßen, B., & Hammer, B. (2018). Linear Supervised Transfer Learning for the Large Margin Nearest Neighbor Classifier. Presented at the SSCI CIDM 2017. doi:10.1109/SSCI.2017.8285359
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  • [26]
    2017 | Datenpublikation | PUB-ID: 2912671 OA
    Paaßen, B., & Schulz, A. (2017). Linear Supervised Transfer Learning Toolbox. Bielefeld University. doi:10.4119/unibi/2912671
    PUB | Dateien verfügbar | DOI
     
  • [25]
    2017 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2909369 OA
    Paaßen, B., Schulz, A., Hahne, J., & Hammer, B. (2017). An EM transfer learning algorithm with applications in bionic hand prostheses. In M. Verleysen (Ed.), Proceedings of the 25th European Symposium on Artificial Neural Networks (ESANN 2017) (pp. 129-134). Bruges: i6doc.com.
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  • [24]
    2017 | Bielefelder E-Dissertation | PUB-ID: 2914256 OA
    Schulz, A. (2017). Discriminative dimensionality reduction: variations, applications, interpretations. Bielefeld: Universität Bielefeld.
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  • [23]
    2017 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2909372 OA
    Schulz, A., Brinkrolf, J., & Hammer, B. (2017). Efficient Kernelization of Discriminative Dimensionality Reduction. Neurocomputing, 268(SI), 34-41. doi:10.1016/j.neucom.2017.01.104
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  • [22]
    2017 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2909037 OA
    Prahm, C., Schulz, A., Paaßen, B., Aszmann, O., Hammer, B., & Dorffner, G. (2017). Echo State Networks as Novel Approach for Low-Cost Myoelectric Control. In A. ten Telje, C. Popow, J. H. Holmes, & L. Sacchi (Eds.), Lecture Notes in Computer Science: Vol. 10259. Proceedings of the 16th Conference on Artificial Intelligence in Medicine (AIME 2017) (pp. 338--342). Springer. doi:10.1007/978-3-319-59758-4_40
    PUB | Dateien verfügbar | DOI
     
  • [21]
    2016 | Konferenzbeitrag | E-Veröff. vor dem Druck | PUB-ID: 2904909 OA
    Schulz, A., & Hammer, B. (2016). Discriminative Dimensionality Reduction in Kernel Space. ESANN2016 Proceedings. 24th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Bruges, Belgium,27-29 April 2016
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  • [20]
    2016 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2905855
    Paaßen, B., Schulz, A., & Hammer, B. (2016). Linear Supervised Transfer Learning for Generalized Matrix LVQ. In B. Hammer, T. Martinetz, & T. Villmann (Eds.), Machine Learning Reports. Proceedings of the Workshop New Challenges in Neural Computation 2016 (pp. 11-18).
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  • [19]
    2016 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2904178 OA
    Prahm, C., Paaßen, B., Schulz, A., Hammer, B., & Aszmann, O. (2016). Transfer Learning for Rapid Re-calibration of a Myoelectric Prosthesis after Electrode Shift. In J. Ibáñez, J. Gonzáles-Vargas, J. M. Azorín, M. Akay, & J. L. Pons (Eds.), Converging Clinical and Engineering Research on Neurorehabilitation II: Proceedings of the 3rd International Conference on NeuroRehabilitation (ICNR2016) (pp. 153--157). Springer. doi:10.1007/978-3-319-46669-9_28
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  • [18]
    2015 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2671047 OA
    Gisbrecht, A., Schulz, A., & Hammer, B. (2015). Parametric nonlinear dimensionality reduction using kernel t-SNE. Neurocomputing, 147, 71-82. doi:10.1016/j.neucom.2013.11.045
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  • [17]
    2015 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2903777 OA
    Schulz, A., Mokbel, B., Biehl, M., & Hammer, B. (2015). Inferring Feature Relevances From Metric Learning. 2015 IEEE Symposium Series on Computational Intelligence Piscataway, NJ: IEEE. doi:10.1109/ssci.2015.225
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  • [16]
    2015 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2766822 OA
    Schulz, A., Gisbrecht, A., & Hammer, B. (2015). Using Discriminative Dimensionality Reduction to Visualize Classifiers. Neural Processing Letters, 42(1), 27-54. doi:10.1007/s11063-014-9394-1
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  • [15]
    2015 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2900303 OA
    Schulz, A., & Hammer, B. (2015). Visualization of Regression Models Using Discriminative Dimensionality Reduction. Computer Analysis of Images and Patterns, Lecture Notes in Computer Science, 9257, 437-449. Cham: Springer Science + Business Media. doi:10.1007/978-3-319-23117-4_38
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  • [14]
    2015 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2900325 OA
    Blöbaum, P., Schulz, A., & Hammer, B. (2015). Unsupervised Dimensionality Reduction for Transfer Learning. In M. Verleysen (Ed.), Proceedings. 23rd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (pp. 507-512). Louvain-la-Neuve: Ciaco.
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  • [13]
    2015 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2900319
    Schulz, A., & Hammer, B. (2015). Discriminative dimensionality reduction for regression problems using the Fisher metric. 2015 International Joint Conference on Neural Networks (IJCNN), 1-8. doi:10.1109/ijcnn.2015.7280736
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  • [12]
    2015 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2783369 OA
    Mokbel, B., & Schulz, A. (2015). Towards Dimensionality Reduction for Smart Home Sensor Data. In B. Hammer, T. Martinetz, & T. Villmann (Eds.), Machine Learning Reports. Proceedings of the Workshop New Challenges in Neural Computation (NC² 2015) (pp. 41-48).
    PUB | PDF | Download (ext.)
     
  • [11]
    2015 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2900318
    Schulz, A., & Hammer, B. (2015). Metric Learning in Dimensionality Reduction. Proceedings of the International Conference on Pattern Recognition Applications and Methods, 232-239. doi:10.5220/0005200802320239
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  • [10]
    2014 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2900320 OA
    Frenay, B., Hofmann, D., Schulz, A., Biehl, M., & Hammer, B. (2014). Valid interpretation of feature relevance for linear data mappings. 2014 IEEE Symposium on Computational Intelligence and Data Mining (CIDM), 149-156. Piscataway, NJ: Institute of Electrical & Electronics Engineers (IEEE). https://doi.org/10.1109/cidm.2014.7008661
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  • [9]
    2014 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2673557
    Schulz, A., Gisbrecht, A., & Hammer, B. (2014). Relevance learning for dimensionality reduction. In M. Verleysen (Ed.), ESANN, 22nd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (pp. 165-170). Bruges, Belgium: i6doc.com.
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  • [8]
    2014 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2900322
    Bloebaum, P., & Schulz, A. (2014). Transfer Learning without given Correspondences. In B. Hammer, T. Martinetz, & T. Villmann (Eds.), Machine Learning Reports. Proceedings of the Workshop New Challenges in Neural Computation (NC² 2014) (pp. 42-51).
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  • [7]
    2014 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2900324
    Gisbrecht, A., Schulz, A., & Hammer, B. (2014). Discriminative Dimensionality Reduction for the Visualization of Classifiers. Pattern Recognition Applications and Methods, Advances in Intelligent Systems and Computing, 318, 39-56. Cham: Springer Science + Business Media. doi:10.1007/978-3-319-12610-4_3
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  • [6]
    2013 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2622454
    Hammer, B., Gisbrecht, A., & Schulz, A. (2013). Applications of discriminative dimensionality reduction. Proceedings of the 2nd International Conference on Pattern Recognition Applications and Methods, 33-41. SCITEPRESS. doi:10.5220/0004245300330041
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  • [5]
    2013 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2622456
    Schulz, A., Gisbrecht, A., & Hammer, B. (2013). Using Nonlinear Dimensionality Reduction to Visualize Classifiers. In I. Rojas, G. Joya, & J. Gabestany (Eds.), Lecture Notes in Computer Science: Vol. 7902. Advances in computational intelligence. Proceedings. Vol 1 (pp. 59-68). Springer. doi:10.1007/978-3-642-38679-4_4
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  • [4]
    2013 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2622466
    Vukanovicz, S., Schulz, A., Haschke, R., & Ritter, H. (2013). Learning the Appropriate Model Population Structures for Locally Weighted Regression. Workshop New Challenges in Neural Computation 2013, Machine Learning Reports, 2013, 87. Bielefeld: Universität Bielefeld.
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  • [3]
    2013 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2622467
    Schulz, A., Gisbrecht, A., & Hammer, B. (2013). Classifier inspection based on different discriminative dimensionality reductions. Workshop NC^2 2013, 77-86
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  • [2]
    2012 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2622449
    Schulz, A., Gisbrecht, A., Bunte, K., & Hammer, B. (2012). How to visualize a classifier? Proceedings of the Workshop - New Challenges in Neural Computation 2012, 73-83
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  • [1]
    2012 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2622453
    Hammer, B., Gisbrecht, A., & Schulz, A. (2012). How to Visualize Large Data Sets?. Presented at the Workshop Advances in Self-Organizing Maps (WSOM), Santiago, Chile. doi:10.1007/978-3-642-35230-0_1
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