Search Results - "Schleif, Frank-Michael"

Passive concept drift handling via variations of learning vector quantization.

Authors: Heusinger, Moritz Raab, Christoph Schleif, Frank-Michael

Source: Neural Computing & Applications; Jan2022, Vol. 34 Issue 1, p89-100, 12p

The evolution of resource-aware cooperation in federated learning.

Authors: Röder, Manuel Lyu, Gengcheng Geiger, Fabian et al.

Source: Neurocomputing; May2026, Vol. 678, pN.PAG-N.PAG, 1p

Learning in federated and dynamic environments: A tutorial on challenges, trends, and practical strategies.

Authors: Polato, Mirko Hammer, Barbara Röder, Manuel et al.

Source: Neurocomputing; Apr2026, Vol. 672, pN.PAG-N.PAG, 1p

Advances in artificial neural networks, machine learning and computational intelligence.

Authors: Oneto, Luca Navarin, Nicoló Schleif, Frank-Michael

Source: Neurocomputing; Oct2022, Vol. 507, p311-314, 4p

Domain adversarial tangent subspace alignment for explainable domain adaptation.

Authors: Raab, Christoph Röder, Manuel Schleif, Frank-Michael

Source: Neurocomputing; Sep2022, Vol. 506, p418-429, 12p

Reactive Soft Prototype Computing for Concept Drift Streams.

Authors: Raab, Christoph Heusinger, Moritz Schleif, Frank-Michael

Source: Neurocomputing; Nov2020, Vol. 416, p340-351, 12p

Transfer learning extensions for the probabilistic classification vector machine.

Authors: Raab, Christoph Schleif, Frank-Michael

Source: Neurocomputing; Jul2020, Vol. 397, p320-330, 11p

Sparsification of core set models in non-metric supervised learning.

Authors: Schleif, Frank-Michael Raab, Christoph Tino, Peter

Source: Pattern Recognition Letters; Jan2020, Vol. 129, p1-7, 7p

Advances in artificial neural networks, machine learning and computational intelligence.

Authors: Oneto, Luca Schleif, Frank-Michael Sperduti, Alessandro

Source: Neurocomputing; Feb2025, Vol. 618, pN.PAG-N.PAG, 1p

Advances in artificial neural networks, machine learning and computational intelligence: Selected papers from the 26th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2018).

Authors: Oneto, Luca Bunte, Kerstin Schleif, Frank-Michael

Source: Neurocomputing; May2019, Vol. 342, p1-5, 5p

Indefinite Proximity Learning: A Review.

Authors: Schleif, Frank-Michael Tino, Peter

Source: Neural Computation. 2015, Vol. 27 Issue 10, p2039-2096. 58p. 5 Diagrams, 5 Charts, 10 Graphs.

Subjects: Machine learning, Data analysis, Big data, Regression analysis, Embeddings (Mathematics)

Data-distribution-informed Nyström approximation for structured data using vector quantization-based landmark determination.

Authors: Münch, Maximilian Bohnsack, Katrin Sophie Schleif, Frank-Michael et al.

Source: Neurocomputing; Sep2024, Vol. 596, pN.PAG-N.PAG, 1p

Evolving trees for the retrieval of mass spectrometry-based bacteria fingerprints.

Authors: Simmuteit, Stephan Schleif, Frank-Michael Villmann, Thomas et al.

Source: Knowledge & Information Systems; Nov2010, Vol. 25 Issue 2, p327-343, 17p, 2 Diagrams, 6 Charts

Classification of mass - spectrometric data in clinical proteomics using learning vector quantization methods.

Authors: Villmann, Thomas Schleif, Frank-Michael Kostrzewa, Markus et al.

Source: Briefings in Bioinformatics. Mar2008, Vol. 9 Issue 2, p129-143. 15p. 7 Diagrams, 2 Charts.

Static and adaptive subspace information fusion for indefinite heterogeneous proximity data.

Authors: Münch, Maximilian Röder, Manuel Heilig, Simon et al.

Source: Neurocomputing; Oct2023, Vol. 555, pN.PAG-N.PAG, 1p

Genome-wide prediction and prioritization of human aging genes by data fusion: a machine learning approach.

Authors: Arabfard, Masoud Ohadi, Mina Rezaei Tabar, Vahid et al.

Source: BMC Genomics. 11/9/2019, Vol. 20 Issue 1, p1-13. 13p. 1 Diagram, 13 Charts, 4 Graphs.