Search Results - "Villmann, Thomas"

Quantum Computing Approaches for Vector Quantization—Current Perspectives and Developments.

Authors: Engelsberger, Alexander Villmann, Thomas

Source: Entropy. Mar2023, Vol. 25 Issue 3, p540. 22p.

Learning vector quantization as an interpretable classifier for the detection of SARS-CoV-2 types based on their RNA sequences.

Authors: Kaden, Marika Bohnsack, Katrin Sophie Weber, Mirko et al.

Source: Neural Computing & Applications; Jan2022, Vol. 34 Issue 1, p67-78, 12p

Quantum-inspired learning vector quantizers for prototype-based classification: Confidential: for personal use only—submitted to Neural Networks and Applications 5/2020.

Authors: Villmann, Thomas Engelsberger, Alexander Ravichandran, Jensun et al.

Source: Neural Computing & Applications; Jan2022, Vol. 34 Issue 1, p79-88, 10p

Prototype-Based Classifiers and Vector Quantization on a Quantum Computer—Implementing Integer Arithmetic Oracles for Nearest Prototype Search.

Authors: Engelsberger, Alexander Pšeničkova, Magdalena Villmann, Thomas

Source: Entropy. Feb2026, Vol. 28 Issue 2, p229. 18p.

FA(IR)2MA-GLVQ – A hidden-feature-bias mitigation approach for fairness in classification learning based on generalized matrix learning vector quantization.

Authors: Kaden, Marika Schubert, Ronny Voigt, Julius et al.

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

Privacy-preserving nearest prototype classifier.

Authors: Schubert, Ronny Davies, Thomas Lange-Geisler, Mandy et al.

Source: Neurocomputing; Mar2026, Vol. 671, pN.PAG-N.PAG, 1p

Explicit Magnification Control of Self-Organizing Maps for “Forbidden” Data.

Authors: Merényi, Erzsébet Jain, Abha Villmann, Thomas

Source: IEEE Transactions on Neural Networks; May 2007, Vol. 18 Issue 3, p786-797, 12p

Neural maps in remote sensing image analysis.

Authors: Villmann, Thomas Merényi, Erzsébet

Source: Neural Networks; April/May 2003, Vol. 16 Issue 3/4, p389-403, 15p

Generalized relevance learning vector quantization.

Authors: Hammer, Barbara Villmann, Thomas

Source: Neural Networks; October/November 2002, Vol. 15 Issue 8/9, p1059-1068, 10p

Topology preservation in self-organizing feature maps: exact definition and measurement.

Authors: Villmann, Thomas Der, Ralf Herrmann, Michael

Source: IEEE Transactions on Neural Networks; March 1997, Vol. 8, p256-266, 11p

Growing a hypercubical output space in a self-organizing feature map.

Authors: Bauer, Hans-Ulrich Villmann, Thomas

Source: IEEE Transactions on Neural Networks; March 1997, Vol. 8, p218-226, 9p

Variants of recurrent learning vector quantization.

Authors: Ravichandran, Jensun Kaden, Marika Villmann, Thomas

Source: Neurocomputing; Sep2022, Vol. 502, p27-36, 10p

Divergence-Based Vector Quantization.

Authors: Villmann, Thomas Haase, Sven

Source: Neural Computation. May2011, Vol. 23 Issue 5, p1343-1392. 50p. 2 Charts, 9 Graphs.

Subjects: Supervised learning, Vector analysis, Classification, Cluster analysis (Statistics), Online education, Algorithms, Functional analysis, Self-organizing maps

Event Related Potentials and Cognitive Evaluation in Wilson's Disease With and Without Neurological Manifestation.

Alternate Title: Nörolojik Bulguları Olan ya da Olmayan Wilson Hastalığı'nda Olaya Bağlı Potansiyeller ve Bilişsel Değerlendirme.

Authors: GÜNTHER, Peter VILLMANN, Thomas HERMANN, Wieland

Source: Journal of Neurological Sciences. 2011, Vol. 28 Issue 1, p79-85. 7p.

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.

Magnification Control in Self-Organizing Maps and Neural Gas.

Authors: Villmann, Thomas Claussen, Jens Christian

Source: Neural Computation. Feb2006, Vol. 18 Issue 2, p446-469. 24p.

Subjects: Self-organizing maps, Artificial neural networks, Self-organizing systems, Learning, Algorithms

Evolutionary algorithms using a neural network like migration scheme.

Authors: Villmann, Thomas

Source: Integrated Computer-Aided Engineering. 2002, Vol. 9 Issue 1, p25. 11p.

Subjects: Algorithms, Artificial neural networks, Iterative methods (Mathematics)

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Variants of DropConnect in Learning vector quantization networks for evaluation of classification stability.

Authors: Ravichandran, Jensun Kaden, Marika Saralajew, Sascha et al.

Source: Neurocomputing; Aug2020, Vol. 403, p121-132, 12p