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WS 2024
LectureTypeSPPSECTS-CreditsCourse number
Advanced Topics ILV 3,5 5,0 M2.08760.11.151
Artificial Neural Networks and Deep Learning (II) ILV 3,5 5,0 M2.08760.11.141
TitelAutorJahr
TitelAutorJahr
Articles in Journals
TitleAuthorYear
Novel Ensemble Feature Selection Techniques Applied to High-Grade Gastroenteropancreatic Neuroendocrine Neoplasms for the Prediction of Survival Computer Methods and Programs in Biomedicine, 244Jenul, A., Stokmo, H., Schrunner, S., Hjortland, G., Revheim, M., Tomic, O.2024
UBayFS: An R Package for User Guided Feature Selection Journal of Open Source Software, 8Jenul, A., Schrunner, S.2023
Principal component-based image segmentation: a new approach to outline in vitro cell colonies Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 11:18-30Arous, D., Schrunner, S., Hanson, I., Edin, N., Malinen, E.2022
A user-guided Bayesian framework for ensemble feature selection in life science applications (UBayFS) Machine Learning, 111:3897-3923Jenul, A., Schrunner, S., Pilz, J., Tomic, O.2022
RENT: A Python Package for Repeated Elastic Net Feature Selection Journal of Open Source Software, 6Jenul, A., Schrunner, S., Huynh, B., Tomic, O.2021
RENT - repeated elastic net technique for feature selection IEEE Access, 9Jenul, A., Schrunner, S., Liland, K., Indahl, U., Futsaether, C., Tomic, O.2021
An explicit solution for image restoration using Markov Random Fields Journal of Signal Processing Systems, 92:257-267Pleschberger, M., Schrunner, S., Pilz, J.2019
Feature extraction from analog wafermaps: a comparison of classical image processing and a deep generative model IEEE Transactions on Semiconductor Manufacturing, 32:190-198Santos, T., Schrunner, S., Geiger, B., Pfeiler, O., Zernig, A., Kästner, A., Kern, R.2019
Conference contributions
TitleAuthorYear
Component Based Pre-filtering of Noisy Data for Improved Tsetlin Machine Modelling in: IEEE (Hrsg.), International Symposium on the Tsetlin Machine (ISTM), 20-21 Jun 2022, Grimstad, NorwayJenul, A., Bhattarai, B., Liland, K., Jiao, L., Schrunner, S., Futsaether, C., Granmo, O., Tomic, O.2022
Ranking Feature-Block Importance in Artificial Multiblock Neural Networks in: Springer Lecture Notes in Computer Science (Hrsg.), International Conference on Artificial Neural Networks 2022, 06-09 Sep 2022, Bristol, UK, S. 163-175Jenul, A., Schrunner, S., Huynh, B., Helin, R., Futsaether, C., Liland, K., Tomic, O.2022
A generative semi-supervised classifier for datasets with unknown classes in: Association for Computing Machinery (Hrsg.), SAC '20: ACM Symposium on Applied Computing 2020, 30 Mar-03 Apr 2020, Brno, Czech Republic, S. 1066-1074Schrunner, S., Geiger, B., Zernig, A., Kern, R.2020
A health factor for process patterns - enhancing semiconductor manufacturing by pattern recognition in analog wafermaps in: IEEE (Hrsg.), IEEE International Conference on Systems, Man and Cybernetics (SMC 2019), 06-09 Oct 2019, Bari, ItalySchrunner, S., Jenul, A., Scheiber, M., Zernig, A., Kästner, A., Kern, R.2019
A comparison of supervised approaches for process pattern recognition in analog semiconductor wafer test data in: IEEE (Hrsg.), IEEE International Conference on Machine Learning and Applications (ICMLA 2018), 17-20 Dec 2018, Orlando, FL, USASchrunner, S., Pfeiler, O., Zernig, A., Kästner, A., Kern, R.2018
Markov random fields for pattern extraction in analog wafer test data in: IEEE (Hrsg.), International Conference on Image Processing Theory, Tools and Applications (IPTA 2017), 28 Nov-01 Dec 2017, Montreal, CanadaSchrunner, S., Pfeiler, O., Zernig, A., Kästner, A., Kern, R.2017
Articles in Journals
TitleAuthorYear
Novel Ensemble Feature Selection Techniques Applied to High-Grade Gastroenteropancreatic Neuroendocrine Neoplasms for the Prediction of Survival Computer Methods and Programs in Biomedicine, 244Jenul, A., Stokmo, H., Schrunner, S., Hjortland, G., Revheim, M., Tomic, O.2024
Articles in Journals
TitleAuthorYear
UBayFS: An R Package for User Guided Feature Selection Journal of Open Source Software, 8Jenul, A., Schrunner, S.2023
Articles in Journals
TitleAuthorYear
Principal component-based image segmentation: a new approach to outline in vitro cell colonies Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 11:18-30Arous, D., Schrunner, S., Hanson, I., Edin, N., Malinen, E.2022
A user-guided Bayesian framework for ensemble feature selection in life science applications (UBayFS) Machine Learning, 111:3897-3923Jenul, A., Schrunner, S., Pilz, J., Tomic, O.2022
Conference contributions
TitleAuthorYear
Component Based Pre-filtering of Noisy Data for Improved Tsetlin Machine Modelling in: IEEE (Hrsg.), International Symposium on the Tsetlin Machine (ISTM), 20-21 Jun 2022, Grimstad, NorwayJenul, A., Bhattarai, B., Liland, K., Jiao, L., Schrunner, S., Futsaether, C., Granmo, O., Tomic, O.2022
Ranking Feature-Block Importance in Artificial Multiblock Neural Networks in: Springer Lecture Notes in Computer Science (Hrsg.), International Conference on Artificial Neural Networks 2022, 06-09 Sep 2022, Bristol, UK, S. 163-175Jenul, A., Schrunner, S., Huynh, B., Helin, R., Futsaether, C., Liland, K., Tomic, O.2022
Articles in Journals
TitleAuthorYear
RENT: A Python Package for Repeated Elastic Net Feature Selection Journal of Open Source Software, 6Jenul, A., Schrunner, S., Huynh, B., Tomic, O.2021
RENT - repeated elastic net technique for feature selection IEEE Access, 9Jenul, A., Schrunner, S., Liland, K., Indahl, U., Futsaether, C., Tomic, O.2021
Conference contributions
TitleAuthorYear
A generative semi-supervised classifier for datasets with unknown classes in: Association for Computing Machinery (Hrsg.), SAC '20: ACM Symposium on Applied Computing 2020, 30 Mar-03 Apr 2020, Brno, Czech Republic, S. 1066-1074Schrunner, S., Geiger, B., Zernig, A., Kern, R.2020
Articles in Journals
TitleAuthorYear
An explicit solution for image restoration using Markov Random Fields Journal of Signal Processing Systems, 92:257-267Pleschberger, M., Schrunner, S., Pilz, J.2019
Feature extraction from analog wafermaps: a comparison of classical image processing and a deep generative model IEEE Transactions on Semiconductor Manufacturing, 32:190-198Santos, T., Schrunner, S., Geiger, B., Pfeiler, O., Zernig, A., Kästner, A., Kern, R.2019
Conference contributions
TitleAuthorYear
A health factor for process patterns - enhancing semiconductor manufacturing by pattern recognition in analog wafermaps in: IEEE (Hrsg.), IEEE International Conference on Systems, Man and Cybernetics (SMC 2019), 06-09 Oct 2019, Bari, ItalySchrunner, S., Jenul, A., Scheiber, M., Zernig, A., Kästner, A., Kern, R.2019
A comparison of supervised approaches for process pattern recognition in analog semiconductor wafer test data in: IEEE (Hrsg.), IEEE International Conference on Machine Learning and Applications (ICMLA 2018), 17-20 Dec 2018, Orlando, FL, USASchrunner, S., Pfeiler, O., Zernig, A., Kästner, A., Kern, R.2018
Markov random fields for pattern extraction in analog wafer test data in: IEEE (Hrsg.), International Conference on Image Processing Theory, Tools and Applications (IPTA 2017), 28 Nov-01 Dec 2017, Montreal, CanadaSchrunner, S., Pfeiler, O., Zernig, A., Kästner, A., Kern, R.2017

Please use this link for external references on the profile of Stefan Schrunner: www.fh-kaernten.at/mitarbeiter-details?person=s.schrunner