Friedhelm Schwenker
Friedhelm Schwenker
Ulm University, Institute of Neural Information Processing
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Cited by
Cited by
Three learning phases for radial-basis-function networks
F Schwenker, HA Kestler, G Palm
Neural networks 14 (4-5), 439-458, 2001
Pattern classification and clustering: A review of partially supervised learning approaches
F Schwenker, E Trentin
Pattern Recognition Letters 37, 4-14, 2014
Semi-supervised learning
MFA Hady, F Schwenker
Handbook on Neural Information Processing, 215-239, 2013
Hierarchical support vector machines for multi-class pattern recognition
F Schwenker
Knowledge-Based Intelligent Engineering Systems and Allied Technologies …, 2000
Multiple classifier systems for the classification of audio-visual emotional states
M Glodek, S Tschechne, G Layher, M Schels, T Brosch, S Scherer, ...
Affective Computing and Intelligent Interaction, 359-368, 2011
A dataset of continuous affect annotations and physiological signals for emotion analysis
K Sharma, C Castellini, EL Van Den Broek, A Albu-Schaeffer, ...
Scientific data 6 (1), 196, 2019
Survey of deep learning in breast cancer image analysis
TG Debelee, F Schwenker, A Ibenthal, D Yohannes
Evolving Systems 11 (1), 143-163, 2020
Iterative retrieval of sparsely coded associative memory patterns
F Schwenker, FT Sommer, G Palm
Neural Networks 9 (3), 445-455, 1996
A survey of brain tumor segmentation and classification algorithms
ES Biratu, F Schwenker, YM Ayano, TG Debelee
Journal of Imaging 7 (9), 179, 2021
Enhanced region growing for brain tumor MR image segmentation
ES Biratu, F Schwenker, TG Debelee, SR Kebede, WG Negera, HT Molla
Journal of Imaging 7 (2), 22, 2021
Neural associative memory
G Palm, F Schwenker, FT Sommer, A Strey, F Kurfeß
Associative processing and processors, 1997
A study of the robustness of KNN classifiers trained using soft labels
N El Gayar, F Schwenker, G Palm
Artificial Neural Networks in Pattern Recognition: Second IAPR Workshop …, 2006
Investigating fuzzy-input fuzzy-output support vector machines for robust voice quality classification
S Scherer, J Kane, C Gobl, F Schwenker
Computer Speech & Language 27 (1), 263-287, 2013
Adaptive confidence learning for the personalization of pain intensity estimation systems
M Kächele, M Amirian, P Thiam, P Werner, S Walter, G Palm, ...
Evolving Systems 8, 71-83, 2017
Methods for person-centered continuous pain intensity assessment from bio-physiological channels
M Kächele, P Thiam, M Amirian, F Schwenker, G Palm
IEEE Journal of Selected Topics in Signal Processing 10 (5), 854-864, 2016
De-noising of high-resolution ECG signals by combining the discrete wavelet transform with the Wiener filter
HA Kestler, M Haschka, W Kratz, F Schwenker, G Palm, V Hombach, ...
Computers in Cardiology 1998. Vol. 25 (Cat. No. 98CH36292), 233-236, 1998
Multimodal emotion classification in naturalistic user behavior
S Walter, S Scherer, M Schels, M Glodek, D Hrabal, M Schmidt, R Böck, ...
Human-Computer Interaction. Towards Mobile and Intelligent Interaction …, 2011
Combining committee-based semi-supervised learning and active learning
M Farouk Abdel Hady, F Schwenker
Journal of Computer Science and Technology 25 (4), 681-698, 2010
Fusion of audio-visual features using hierarchical classifier systems for the recognition of affective states and the state of depression
M Kächele, M Glodek, D Zharkov, S Meudt, F Schwenker
depression 1 (1), 671-678, 2014
Co-training by committee: a new semi-supervised learning framework
MFA Hady, F Schwenker
2008 IEEE International Conference on Data Mining Workshops, 563-572, 2008
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