| Reactive soft prototype computing for concept drift streams C Raab, M Heusinger, FM Schleif Neurocomputing 416, 340-351, 2020 | 115 | 2020 |
| Passive concept drift handling via variations of learning vector quantization M Heusinger, C Raab, FM Schleif Neural Computing and Applications 34 (1), 89-100, 2022 | 20 | 2022 |
| Passive concept drift handling via momentum based robust soft learning vector quantization M Heusinger, C Raab, FM Schleif Advances in Self-Organizing Maps, Learning Vector Quantization, Clustering …, 2020 | 18 | 2020 |
| Bridging adversarial and statistical domain transfer via spectral adaptation networks C Raab, P Vath, P Meier, FM Schleif Proceedings of the Asian Conference on Computer Vision, 2020 | 13 | 2020 |
| Reactive Soft Prototype Computing for frequent reoccurring Concept Drift. C Raab, M Heusinger, FM Schleif ESANN, 2019 | 13 | 2019 |
| Data-driven supervised learning for life science data M Münch, C Raab, M Biehl, FM Schleif Frontiers in Applied Mathematics and Statistics 6, 553000, 2020 | 10 | 2020 |
| Transfer learning extensions for the probabilistic classification vector machine C Raab, FM Schleif Neurocomputing 397, 320-330, 2020 | 8 | 2020 |
| Low-rank subspace override for unsupervised domain adaptation C Raab, FM Schleif KI 2020: Advances in Artificial Intelligence: 43rd German Conference on AI …, 2020 | 7 | 2020 |
| Sparsification of core set models in non-metric supervised learning FM Schleif, C Raab, P Tino Pattern Recognition Letters 129, 1-7, 2020 | 7 | 2020 |
| Domain adversarial tangent subspace alignment for explainable domain adaptation C Raab, M Röder, FM Schleif Neurocomputing 506, 418-429, 2022 | 6 | 2022 |
| Dimensionality reduction in the context of dynamic social media data streams M Heusinger, C Raab, FM Schleif Evolving Systems 13 (3), 387-401, 2022 | 6 | 2022 |
| Transfer learning for the probabilistic classification vector machine C Raab, FM Schleif Conformal and probabilistic prediction and applications, 187-200, 2018 | 6 | 2018 |
| Sparse transfer classification for text documents C Raab, FM Schleif KI 2018: Advances in Artificial Intelligence: 41st German Conference on AI …, 2018 | 6 | 2018 |
| PROVAL: A framework for comparison of protein sequence embeddings P Väth, M Münch, C Raab, FM Schleif Journal of Computational Mathematics and Data Science 3, 100044, 2022 | 5 | 2022 |
| Analyzing dynamic social media data via random projection-a new challenge for stream classifiers M Heusinger, C Raab, FM Schleif 2020 IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS), 1-8, 2020 | 5 | 2020 |
| Structure preserving encoding of non-euclidean similarity data M Münch, C Raab, M Biehl, FM Schleif The 9th International Conference on Pattern Recognition Applications and …, 2020 | 5 | 2020 |
| Federated Learning--Methods, Applications and beyond M Heusinger, C Raab, F Rossi, FM Schleif arXiv preprint arXiv:2212.11729, 2022 | 3 | 2022 |
| Encoding of indefinite proximity data: A structure preserving perspective M Münch, C Raab, FM Schleif Pattern Recognition Applications and Methods: 9th International Conference …, 2020 | 3 | 2020 |
| Sparsification of indefinite learning models FM Schleif, C Raab, P Tino Structural, Syntactic, and Statistical Pattern Recognition: Joint IAPR …, 2018 | 3 | 2018 |
| Domain adaptation via low-rank basis approximation C Raab, FM Schleif | 2 | 2019 |
Frank-Michael SchleifProfessor of Computational Intelligence, Technical-UAS Würzburg-SchweinfurtVerified email at thws.de
