33 Publikationen

Alle markieren

[33]
2020 | Zeitschriftenaufsatz | E-Veröff. vor dem Druck | PUB-ID: 2944327
A. Panda, et al., “Tissue- and development-stage-specific mRNA and heterogeneous CNV signatures of human ribosomal proteins in normal and cancer samples.”, Nucleic acids research, 2020.
PUB | DOI | PubMed | Europe PMC
 
[32]
2020 | Konferenzbeitrag | PUB-ID: 2943260
A. Schulz, F. Hinder, and B. Hammer, “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}, 2020.
PUB | DOI | Download (ext.)
 
[31]
2020 | Konferenzbeitrag | Im Druck | PUB-ID: 2941931
B. Paaßen and A. Schulz, “Reservoir memory machines”, Proceedings of the 28th European Symposium on Artificial Neural Networks (ESANN 2020), M. Verleysen, ed., Bruges: In Press.
PUB | Download (ext.) | arXiv
 
[30]
2019 | Zeitschriftenaufsatz | E-Veröff. vor dem Druck | PUB-ID: 2934458 OA
C. Prahm, et al., “Counteracting Electrode Shifts in Upper-Limb Prosthesis Control via Transfer Learning”, IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 27, 2019, pp. 956-962.
PUB | PDF | DOI | WoS | PubMed | Europe PMC
 
[29]
2018 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2914505
B. Paaßen, et al., “Expectation maximization transfer learning and its application for bionic hand prostheses”, Neurocomputing, vol. 298, 2018, pp. 122-133.
PUB | DOI | Download (ext.) | WoS | arXiv
 
[28]
2018 | Konferenzbeitrag | PUB-ID: 2930001 OA
A. Schulz, et al., “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), 2018.
PUB | PDF
 
[27]
2018 | Konferenzbeitrag | PUB-ID: 2916318
K. Berger, et al., “Linear Supervised Transfer Learning for the Large Margin Nearest Neighbor Classifier”, Presented at the SSCI CIDM 2017, 2018.
PUB | DOI
 
[26]
2017 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2909369 OA
B. Paaßen, et al., “An EM transfer learning algorithm with applications in bionic hand prostheses”, Proceedings of the 25th European Symposium on Artificial Neural Networks (ESANN 2017), M. Verleysen, ed., Bruges: i6doc.com, 2017, pp.129-134.
PUB | PDF
 
[25]
2017 | Datenpublikation | PUB-ID: 2912671 OA
B. Paaßen and A. Schulz, Linear Supervised Transfer Learning Toolbox, Bielefeld University, 2017.
PUB | Dateien verfügbar | DOI
 
[24]
2017 | Bielefelder E-Dissertation | PUB-ID: 2914256 OA
A. Schulz, Discriminative dimensionality reduction: variations, applications, interpretations, Bielefeld: Universität Bielefeld, 2017.
PUB | PDF
 
[23]
2017 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2909372 OA
A. Schulz, J. Brinkrolf, and B. Hammer, “Efficient Kernelization of Discriminative Dimensionality Reduction”, Neurocomputing, vol. 268, 2017, pp. 34-41.
PUB | PDF | DOI | WoS
 
[22]
2017 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2909037 OA
C. Prahm, et al., “Echo State Networks as Novel Approach for Low-Cost Myoelectric Control”, Proceedings of the 16th Conference on Artificial Intelligence in Medicine (AIME 2017), A. ten Telje, et al., eds., Lecture Notes in Computer Science, vol. 10259, Springer, 2017, pp.338--342.
PUB | Dateien verfügbar | DOI
 
[21]
2016 | Konferenzbeitrag | E-Veröff. vor dem Druck | PUB-ID: 2904909 OA
A. Schulz and B. Hammer, “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, i6doc.com, 2016.
PUB | PDF
 
[20]
2016 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2905855
B. Paaßen, A. Schulz, and B. Hammer, “Linear Supervised Transfer Learning for Generalized Matrix LVQ”, Proceedings of the Workshop New Challenges in Neural Computation 2016, B. Hammer, T. Martinetz, and T. Villmann, eds., Machine Learning Reports, 2016, pp.11-18.
PUB | Download (ext.)
 
[19]
2016 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2904178 OA
C. Prahm, et al., “Transfer Learning for Rapid Re-calibration of a Myoelectric Prosthesis after Electrode Shift”, Converging Clinical and Engineering Research on Neurorehabilitation II: Proceedings of the 3rd International Conference on NeuroRehabilitation (ICNR2016), J. Ibáñez, et al., eds., Springer, 2016, pp.153--157.
PUB | PDF | DOI | Download (ext.)
 
[18]
2015 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2671047 OA
A. Gisbrecht, A. Schulz, and B. Hammer, “Parametric nonlinear dimensionality reduction using kernel t-SNE”, Neurocomputing, vol. 147, 2015, pp. 71-82.
PUB | PDF | DOI | WoS
 
[17]
2015 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2903777 OA
A. Schulz, et al., “Inferring Feature Relevances From Metric Learning”, 2015 IEEE Symposium Series on Computational Intelligence, Piscataway, NJ: IEEE, 2015.
PUB | PDF | DOI
 
[16]
2015 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2900303 OA
A. Schulz and B. Hammer, “Visualization of Regression Models Using Discriminative Dimensionality Reduction”, Computer Analysis of Images and Patterns, Lecture Notes in Computer Science, vol. 9257, Cham: Springer Science + Business Media, 2015, pp.437-449.
PUB | PDF | DOI
 
[15]
2015 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2766822 OA
A. Schulz, A. Gisbrecht, and B. Hammer, “Using Discriminative Dimensionality Reduction to Visualize Classifiers”, Neural Processing Letters, vol. 42, 2015, pp. 27-54.
PUB | PDF | DOI | WoS
 
[14]
2015 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2900325 OA
P. Blöbaum, A. Schulz, and B. Hammer, “Unsupervised Dimensionality Reduction for Transfer Learning”, Proceedings. 23rd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, M. Verleysen, ed., Louvain-la-Neuve: Ciaco, 2015, pp.507-512.
PUB | PDF
 
[13]
2015 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2900319
A. Schulz and B. Hammer, “Discriminative dimensionality reduction for regression problems using the Fisher metric”, 2015 International Joint Conference on Neural Networks (IJCNN), Institute of Electrical & Electronics Engineers (IEEE), 2015, pp.1-8.
PUB | DOI
 
[12]
2015 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2783369 OA
B. Mokbel and A. Schulz, “Towards Dimensionality Reduction for Smart Home Sensor Data”, Proceedings of the Workshop New Challenges in Neural Computation (NC² 2015), B. Hammer, T. Martinetz, and T. Villmann, eds., Machine Learning Reports, 2015, pp.41-48.
PUB | PDF | Download (ext.)
 
[11]
2015 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2900318
A. Schulz and B. Hammer, “Metric Learning in Dimensionality Reduction”, Proceedings of the International Conference on Pattern Recognition Applications and Methods, Scitepress, 2015, pp.232-239.
PUB | DOI
 
[10]
2014 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2900320
B. Frenay, et al., “Valid interpretation of feature relevance for linear data mappings”, 2014 IEEE Symposium on Computational Intelligence and Data Mining (CIDM), Institute of Electrical & Electronics Engineers (IEEE), 2014, pp.149-156.
PUB | DOI
 
[9]
2014 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2673557
A. Schulz, A. Gisbrecht, and B. Hammer, “Relevance learning for dimensionality reduction”, ESANN, 22nd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, M. Verleysen, ed., Bruges, Belgium: i6doc.com, 2014, pp.165-170.
PUB
 
[8]
2014 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2900322
P. Bloebaum and A. Schulz, “Transfer Learning without given Correspondences”, Proceedings of the Workshop New Challenges in Neural Computation (NC² 2014), B. Hammer, T. Martinetz, and T. Villmann, eds., Machine Learning Reports, 2014, pp.42-51.
PUB
 
[7]
2014 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2900324
A. Gisbrecht, A. Schulz, and B. Hammer, “Discriminative Dimensionality Reduction for the Visualization of Classifiers”, Pattern Recognition Applications and Methods, Advances in Intelligent Systems and Computing, vol. 318, Cham: Springer Science + Business Media, 2014, pp.39-56.
PUB | DOI
 
[6]
2013 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2622454
B. Hammer, A. Gisbrecht, and A. Schulz, “Applications of discriminative dimensionality reduction”, Proceedings of the 2nd International Conference on Pattern Recognition Applications and Methods, SCITEPRESS, 2013, pp.33-41.
PUB | DOI
 
[5]
2013 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2622456
A. Schulz, A. Gisbrecht, and B. Hammer, “Using Nonlinear Dimensionality Reduction to Visualize Classifiers”, Advances in computational intelligence. Proceedings. Vol 1, I. Rojas, G. Joya, and J. Gabestany, eds., Lecture Notes in Computer Science, vol. 7902, Springer, 2013, pp.59-68.
PUB | DOI | WoS
 
[4]
2013 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2622466
S. Vukanovicz, et al., “Learning the Appropriate Model Population Structures for Locally Weighted Regression”, Workshop New Challenges in Neural Computation 2013, Machine Learning Reports, vol. 2013, Bielefeld: Universität Bielefeld, 2013, pp.87.
PUB
 
[3]
2013 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2622467
A. Schulz, A. Gisbrecht, and B. Hammer, “Classifier inspection based on different discriminative dimensionality reductions”, Workshop NC^2 2013, TR Machine Learning Reports, 2013, pp.77-86.
PUB
 
[2]
2012 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2622449
A. Schulz, et al., “How to visualize a classifier?”, Proceedings of the Workshop - New Challenges in Neural Computation 2012, Machine Learning Reports, 2012, pp.73-83.
PUB
 
[1]
2012 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2622453
B. Hammer, A. Gisbrecht, and A. Schulz, “How to Visualize Large Data Sets?”, Presented at the Workshop Advances in Self-Organizing Maps (WSOM), Santiago, Chile, 2012.
PUB | DOI
 

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33 Publikationen

Alle markieren

[33]
2020 | Zeitschriftenaufsatz | E-Veröff. vor dem Druck | PUB-ID: 2944327
A. Panda, et al., “Tissue- and development-stage-specific mRNA and heterogeneous CNV signatures of human ribosomal proteins in normal and cancer samples.”, Nucleic acids research, 2020.
PUB | DOI | PubMed | Europe PMC
 
[32]
2020 | Konferenzbeitrag | PUB-ID: 2943260
A. Schulz, F. Hinder, and B. Hammer, “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}, 2020.
PUB | DOI | Download (ext.)
 
[31]
2020 | Konferenzbeitrag | Im Druck | PUB-ID: 2941931
B. Paaßen and A. Schulz, “Reservoir memory machines”, Proceedings of the 28th European Symposium on Artificial Neural Networks (ESANN 2020), M. Verleysen, ed., Bruges: In Press.
PUB | Download (ext.) | arXiv
 
[30]
2019 | Zeitschriftenaufsatz | E-Veröff. vor dem Druck | PUB-ID: 2934458 OA
C. Prahm, et al., “Counteracting Electrode Shifts in Upper-Limb Prosthesis Control via Transfer Learning”, IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 27, 2019, pp. 956-962.
PUB | PDF | DOI | WoS | PubMed | Europe PMC
 
[29]
2018 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2914505
B. Paaßen, et al., “Expectation maximization transfer learning and its application for bionic hand prostheses”, Neurocomputing, vol. 298, 2018, pp. 122-133.
PUB | DOI | Download (ext.) | WoS | arXiv
 
[28]
2018 | Konferenzbeitrag | PUB-ID: 2930001 OA
A. Schulz, et al., “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), 2018.
PUB | PDF
 
[27]
2018 | Konferenzbeitrag | PUB-ID: 2916318
K. Berger, et al., “Linear Supervised Transfer Learning for the Large Margin Nearest Neighbor Classifier”, Presented at the SSCI CIDM 2017, 2018.
PUB | DOI
 
[26]
2017 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2909369 OA
B. Paaßen, et al., “An EM transfer learning algorithm with applications in bionic hand prostheses”, Proceedings of the 25th European Symposium on Artificial Neural Networks (ESANN 2017), M. Verleysen, ed., Bruges: i6doc.com, 2017, pp.129-134.
PUB | PDF
 
[25]
2017 | Datenpublikation | PUB-ID: 2912671 OA
B. Paaßen and A. Schulz, Linear Supervised Transfer Learning Toolbox, Bielefeld University, 2017.
PUB | Dateien verfügbar | DOI
 
[24]
2017 | Bielefelder E-Dissertation | PUB-ID: 2914256 OA
A. Schulz, Discriminative dimensionality reduction: variations, applications, interpretations, Bielefeld: Universität Bielefeld, 2017.
PUB | PDF
 
[23]
2017 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2909372 OA
A. Schulz, J. Brinkrolf, and B. Hammer, “Efficient Kernelization of Discriminative Dimensionality Reduction”, Neurocomputing, vol. 268, 2017, pp. 34-41.
PUB | PDF | DOI | WoS
 
[22]
2017 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2909037 OA
C. Prahm, et al., “Echo State Networks as Novel Approach for Low-Cost Myoelectric Control”, Proceedings of the 16th Conference on Artificial Intelligence in Medicine (AIME 2017), A. ten Telje, et al., eds., Lecture Notes in Computer Science, vol. 10259, Springer, 2017, pp.338--342.
PUB | Dateien verfügbar | DOI
 
[21]
2016 | Konferenzbeitrag | E-Veröff. vor dem Druck | PUB-ID: 2904909 OA
A. Schulz and B. Hammer, “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, i6doc.com, 2016.
PUB | PDF
 
[20]
2016 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2905855
B. Paaßen, A. Schulz, and B. Hammer, “Linear Supervised Transfer Learning for Generalized Matrix LVQ”, Proceedings of the Workshop New Challenges in Neural Computation 2016, B. Hammer, T. Martinetz, and T. Villmann, eds., Machine Learning Reports, 2016, pp.11-18.
PUB | Download (ext.)
 
[19]
2016 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2904178 OA
C. Prahm, et al., “Transfer Learning for Rapid Re-calibration of a Myoelectric Prosthesis after Electrode Shift”, Converging Clinical and Engineering Research on Neurorehabilitation II: Proceedings of the 3rd International Conference on NeuroRehabilitation (ICNR2016), J. Ibáñez, et al., eds., Springer, 2016, pp.153--157.
PUB | PDF | DOI | Download (ext.)
 
[18]
2015 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2671047 OA
A. Gisbrecht, A. Schulz, and B. Hammer, “Parametric nonlinear dimensionality reduction using kernel t-SNE”, Neurocomputing, vol. 147, 2015, pp. 71-82.
PUB | PDF | DOI | WoS
 
[17]
2015 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2903777 OA
A. Schulz, et al., “Inferring Feature Relevances From Metric Learning”, 2015 IEEE Symposium Series on Computational Intelligence, Piscataway, NJ: IEEE, 2015.
PUB | PDF | DOI
 
[16]
2015 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2900303 OA
A. Schulz and B. Hammer, “Visualization of Regression Models Using Discriminative Dimensionality Reduction”, Computer Analysis of Images and Patterns, Lecture Notes in Computer Science, vol. 9257, Cham: Springer Science + Business Media, 2015, pp.437-449.
PUB | PDF | DOI
 
[15]
2015 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2766822 OA
A. Schulz, A. Gisbrecht, and B. Hammer, “Using Discriminative Dimensionality Reduction to Visualize Classifiers”, Neural Processing Letters, vol. 42, 2015, pp. 27-54.
PUB | PDF | DOI | WoS
 
[14]
2015 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2900325 OA
P. Blöbaum, A. Schulz, and B. Hammer, “Unsupervised Dimensionality Reduction for Transfer Learning”, Proceedings. 23rd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, M. Verleysen, ed., Louvain-la-Neuve: Ciaco, 2015, pp.507-512.
PUB | PDF
 
[13]
2015 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2900319
A. Schulz and B. Hammer, “Discriminative dimensionality reduction for regression problems using the Fisher metric”, 2015 International Joint Conference on Neural Networks (IJCNN), Institute of Electrical & Electronics Engineers (IEEE), 2015, pp.1-8.
PUB | DOI
 
[12]
2015 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2783369 OA
B. Mokbel and A. Schulz, “Towards Dimensionality Reduction for Smart Home Sensor Data”, Proceedings of the Workshop New Challenges in Neural Computation (NC² 2015), B. Hammer, T. Martinetz, and T. Villmann, eds., Machine Learning Reports, 2015, pp.41-48.
PUB | PDF | Download (ext.)
 
[11]
2015 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2900318
A. Schulz and B. Hammer, “Metric Learning in Dimensionality Reduction”, Proceedings of the International Conference on Pattern Recognition Applications and Methods, Scitepress, 2015, pp.232-239.
PUB | DOI
 
[10]
2014 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2900320
B. Frenay, et al., “Valid interpretation of feature relevance for linear data mappings”, 2014 IEEE Symposium on Computational Intelligence and Data Mining (CIDM), Institute of Electrical & Electronics Engineers (IEEE), 2014, pp.149-156.
PUB | DOI
 
[9]
2014 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2673557
A. Schulz, A. Gisbrecht, and B. Hammer, “Relevance learning for dimensionality reduction”, ESANN, 22nd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, M. Verleysen, ed., Bruges, Belgium: i6doc.com, 2014, pp.165-170.
PUB
 
[8]
2014 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2900322
P. Bloebaum and A. Schulz, “Transfer Learning without given Correspondences”, Proceedings of the Workshop New Challenges in Neural Computation (NC² 2014), B. Hammer, T. Martinetz, and T. Villmann, eds., Machine Learning Reports, 2014, pp.42-51.
PUB
 
[7]
2014 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2900324
A. Gisbrecht, A. Schulz, and B. Hammer, “Discriminative Dimensionality Reduction for the Visualization of Classifiers”, Pattern Recognition Applications and Methods, Advances in Intelligent Systems and Computing, vol. 318, Cham: Springer Science + Business Media, 2014, pp.39-56.
PUB | DOI
 
[6]
2013 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2622454
B. Hammer, A. Gisbrecht, and A. Schulz, “Applications of discriminative dimensionality reduction”, Proceedings of the 2nd International Conference on Pattern Recognition Applications and Methods, SCITEPRESS, 2013, pp.33-41.
PUB | DOI
 
[5]
2013 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2622456
A. Schulz, A. Gisbrecht, and B. Hammer, “Using Nonlinear Dimensionality Reduction to Visualize Classifiers”, Advances in computational intelligence. Proceedings. Vol 1, I. Rojas, G. Joya, and J. Gabestany, eds., Lecture Notes in Computer Science, vol. 7902, Springer, 2013, pp.59-68.
PUB | DOI | WoS
 
[4]
2013 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2622466
S. Vukanovicz, et al., “Learning the Appropriate Model Population Structures for Locally Weighted Regression”, Workshop New Challenges in Neural Computation 2013, Machine Learning Reports, vol. 2013, Bielefeld: Universität Bielefeld, 2013, pp.87.
PUB
 
[3]
2013 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2622467
A. Schulz, A. Gisbrecht, and B. Hammer, “Classifier inspection based on different discriminative dimensionality reductions”, Workshop NC^2 2013, TR Machine Learning Reports, 2013, pp.77-86.
PUB
 
[2]
2012 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2622449
A. Schulz, et al., “How to visualize a classifier?”, Proceedings of the Workshop - New Challenges in Neural Computation 2012, Machine Learning Reports, 2012, pp.73-83.
PUB
 
[1]
2012 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2622453
B. Hammer, A. Gisbrecht, and A. Schulz, “How to Visualize Large Data Sets?”, Presented at the Workshop Advances in Self-Organizing Maps (WSOM), Santiago, Chile, 2012.
PUB | DOI
 

Suche

Publikationen filtern

Darstellung / Sortierung

Zitationsstil: ieee

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