T. Villmann

发表

Fuzzy Classification for Classification of Mass Spectrometric Data Based on Learning Vector Quantization

Frank-Michael Schleif, Barbara Hammer, T. Villmann, 2005 .

Machine Learning and Soft-Computing in Bioinformatics. A Short Journey

Frank-Michael Schleif, Barbara Hammer, T. Villmann, 2006 .

19th European Symposium on Artificial Neural Networks (ESANN 2011)

Michael Biehl, Barbara Hammer, T. Villmann, 2011 .

Metric adaptation and relevance learning in learning vector quantization

Barbara Hammer, T. Villmann, T. Villmann, 2003 .

Learning matrix quantization and relevance learning based on Schatten-p-norms

T. Villmann, M. Lange, A. Bohnsack, 2016, Neurocomputing.

MIWOCI 2010, Mittweida Workshop on Computational Intelligence

T. Villmann, F. M. Schleif, U. Seiffert, 2010 .

Proc. 5th International Workshop on Computational Systems Biology

Michael Biehl, T. Villmann, Marc Strickert, 2008 .

Hierarchical evolving trees together with global and local learning for large data sets in MALDI imaging

Frank-Michael Schleif, T. Villmann, Stephan Simmuteit, 2010 .

10th Int. Work-Conference on Artificial Neural Networks (IWANN 2009)

Michael Biehl, Frank-Michael Schleif, T. Villmann, 2009 .

Learning vector quantization and relevances in complex coefficient space

T. Villmann, M. Kaden, U. Seiffert, 2019, Neural Computing and Applications.

Parallel Evolutionary Algorithms with SOM-Like Migration and their Application to Real World Data Sets

T. Villmann, K. Hering, R. Haupt, 1999, ICANNGA.

Proc. 21st Europ. Symp. Artificial Neural Networks (ESANN)

Michael Biehl, T. Villmann, Mandy Lange, 2013 .

Neural gas clustering for sparse proximity data

Frank-Michael Schleif, Barbara Hammer, Alexander Hasenfuss, 2007 .

Prototype based recognition of splice sites

Barbara Hammer, T. Villmann, Marc Strickert, 2005 .

ROC-Optimization and Statistical Quality Measures in Learning Vector Quantization Classifiers

Michael Biehl, T. Villmann, Marika Kaden, 2014 .

Supervised Neural Gas for Functional Data and its Application to the Analysis of Clinical Proteom Spectra

Frank-Michael Schleif, Barbara Hammer, T. Villmann, 2007 .

Supervised Neural Gas and Relevance Learning in Learning Vector Quantization

Frank-Michael Schleif, Barbara Hammer, T. Villmann, 2003 .

Pattern Recognition by Supervised Relevance Neural Gas and its Application to Spectral Data in Bioinformatics

Frank-Michael Schleif, Barbara Hammer, T. Villmann, 2008 .

DropConnect for Evaluation of Classification Stability in Learning Vector Quantization

T. Villmann, J. Ravichandran, S. Saralajew, 2019, ESANN.

Supervised Neural Gas for Learning Vector Quantization

Barbara Hammer, T. Villmann, T. Villmann, 2002 .

Metric adaptation for optimal feature classification in learning vector quantization applied to environment detection

Frank-Michael Schleif, Barbara Hammer, T. Villmann, 2004 .

Association Learning in SOMs for Fuzzy-Classification

Frank-Michael Schleif, T. Villmann, Rob A. E. M. Tollenaar, 2007, ICMLA 2007.

Supervised data analysis and reliability estimation for spectral data

Frank-Michael Schleif, T. Villmann, T. Villmann, 2009 .

Comparison of clinical types of Wilson's disease and glucose metabolism in extrapyramidal motor brain regions

T. Villmann, T. Villmann, S. Hesse, 2002, Journal of Neurology.

Fuzzy Labeled Neural GAS for Fuzzy Classification

Frank-Michael Schleif, Barbara Hammer, T. Villmann, 2005 .

Automatic Identification and Quantification of Metabolites in H-NMR Measurements

Frank-Michael Schleif, T. Villmann, Michael Cross, 2008 .