33 Publikationen

Alle markieren

[33]
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 | PubMed | Europe PMC
 
[32]
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}. doi:https://doi.org/10.24963/ijcai.2020/319
PUB | DOI | Download (ext.)
 
[31]
2020 | Konferenzbeitrag | Im Druck | PUB-ID: 2941931
Paaßen, B., & Schulz, A. (In Press). Reservoir memory machines. In M. Verleysen (Ed.), Proceedings of the 28th European Symposium on Artificial Neural Networks (ESANN 2020) Bruges.
PUB | Download (ext.) | arXiv
 
[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
PUB | PDF | DOI | WoS | PubMed | Europe PMC
 
[29]
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
 
[28]
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).
PUB | PDF
 
[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
PUB | DOI
 
[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.
PUB | PDF
 
[24]
2017 | Bielefelder E-Dissertation | PUB-ID: 2914256 OA
Schulz, A. (2017). Discriminative dimensionality reduction: variations, applications, interpretations. Bielefeld: Universität Bielefeld.
PUB | PDF
 
[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
PUB | PDF | DOI | WoS
 
[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
PUB | PDF
 
[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).
PUB | Download (ext.)
 
[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
PUB | PDF | DOI | Download (ext.)
 
[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
PUB | PDF | DOI | WoS
 
[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
PUB | PDF | DOI
 
[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
PUB | PDF | DOI | WoS
 
[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
PUB | PDF | DOI
 
[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.
PUB | PDF
 
[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
PUB | DOI
 
[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
PUB | DOI
 
[10]
2014 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2900320
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. doi:10.1109/cidm.2014.7008661
PUB | DOI
 
[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.
PUB
 
[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).
PUB
 
[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
PUB | DOI
 
[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
PUB | DOI
 
[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
PUB | DOI | WoS
 
[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.
PUB
 
[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
PUB
 
[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
PUB
 
[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
PUB | DOI
 

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33 Publikationen

Alle markieren

[33]
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 | PubMed | Europe PMC
 
[32]
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}. doi:https://doi.org/10.24963/ijcai.2020/319
PUB | DOI | Download (ext.)
 
[31]
2020 | Konferenzbeitrag | Im Druck | PUB-ID: 2941931
Paaßen, B., & Schulz, A. (In Press). Reservoir memory machines. In M. Verleysen (Ed.), Proceedings of the 28th European Symposium on Artificial Neural Networks (ESANN 2020) Bruges.
PUB | Download (ext.) | arXiv
 
[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
PUB | PDF | DOI | WoS | PubMed | Europe PMC
 
[29]
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
 
[28]
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).
PUB | PDF
 
[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
PUB | DOI
 
[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.
PUB | PDF
 
[24]
2017 | Bielefelder E-Dissertation | PUB-ID: 2914256 OA
Schulz, A. (2017). Discriminative dimensionality reduction: variations, applications, interpretations. Bielefeld: Universität Bielefeld.
PUB | PDF
 
[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
PUB | PDF | DOI | WoS
 
[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
PUB | PDF
 
[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).
PUB | Download (ext.)
 
[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
PUB | PDF | DOI | Download (ext.)
 
[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
PUB | PDF | DOI | WoS
 
[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
PUB | PDF | DOI
 
[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
PUB | PDF | DOI | WoS
 
[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
PUB | PDF | DOI
 
[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.
PUB | PDF
 
[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
PUB | DOI
 
[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
PUB | DOI
 
[10]
2014 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2900320
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. doi:10.1109/cidm.2014.7008661
PUB | DOI
 
[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.
PUB
 
[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).
PUB
 
[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
PUB | DOI
 
[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
PUB | DOI
 
[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
PUB | DOI | WoS
 
[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.
PUB
 
[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
PUB
 
[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
PUB
 
[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
PUB | DOI
 

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