His scientific interests lie mostly in Artificial intelligence, Vector quantization, Learning vector quantization, Artificial neural network and Pattern recognition. His studies deal with areas such as Machine learning and Divergence as well as Artificial intelligence. His research on Vector quantization also deals with topics like
His Learning vector quantization research incorporates elements of Euclidean distance, Similarity measure, Data mining and One-class classification. His Artificial neural network study combines topics in areas such as Deep learning and Computer vision. In general Pattern recognition study, his work on Discriminative model often relates to the realm of Square matrix, thereby connecting several areas of interest.
Thomas Villmann mainly focuses on Artificial intelligence, Learning vector quantization, Pattern recognition, Machine learning and Vector quantization. His study in Artificial neural network, Cluster analysis, Neural gas, Semi-supervised learning and Self-organizing map is carried out as part of his studies in Artificial intelligence. The concepts of his Learning vector quantization study are interwoven with issues in Gradient descent, Matrix, Function and Support vector machine.
His work in Pattern recognition tackles topics such as Data mining which are related to areas like Fuzzy classification. His work on Machine learning deals in particular with Unsupervised learning, Supervised learning, Online machine learning, Linear classifier and Competitive learning. His Vector quantization study combines topics from a wide range of disciplines, such as Metric and Euclidean distance.
His primary areas of investigation include Artificial intelligence, Learning vector quantization, Machine learning, Pattern recognition and Semi-supervised learning. His study in Artificial intelligence focuses on Vector quantization, Classifier, Interpretability, Probabilistic logic and Artificial neural network. His work in the fields of Linde–Buzo–Gray algorithm overlaps with other areas such as Generalized linear array model.
His Learning vector quantization study is concerned with the larger field of Algorithm. His study in the fields of Self-organizing map under the domain of Machine learning overlaps with other disciplines such as Context and Process. His work on Support vector machine, Euclidean distance, Feature vector and Class as part of general Pattern recognition research is frequently linked to Weighting, thereby connecting diverse disciplines of science.
Thomas Villmann mainly investigates Artificial intelligence, Learning vector quantization, Machine learning, Pattern recognition and Vector quantization. His research on Artificial intelligence frequently connects to adjacent areas such as Extension. His research in Learning vector quantization intersects with topics in Semi-supervised learning, Online machine learning, Classifier and Perceptron.
In Machine learning, Thomas Villmann works on issues like Probabilistic logic, which are connected to Training set, Maximization, Cross entropy and Medical diagnosis. His Pattern recognition study integrates concerns from other disciplines, such as Equivalence and Data pre-processing. His Vector quantization research is multidisciplinary, incorporating elements of Binary classification, Discriminant, Discriminant function analysis and Stochastic gradient descent.
This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.
Generalized relevance learning vector quantization
Barbara Hammer;Thomas Villmann.
Neural Networks (2002)
Topology preservation in self-organizing feature maps: exact definition and measurement
T. Villmann;R. Der;M. Herrmann;T.M. Martinetz.
IEEE Transactions on Neural Networks (1997)
Neural maps in remote sensing image analysis
Thomas Villmann;Erzsébet Merényi;Barbara Hammer.
Neural Networks (2003)
Growing a hypercubical output space in a self-organizing feature map
H.-U. Bauer;T. Villmann.
IEEE Transactions on Neural Networks (1997)
Serotonin and dopamine transporter imaging in patients with obsessive-compulsive disorder.
Swen Hesse;Ulrich Müller;Ulrich Müller;Thomas Lincke;Henryk Barthel.
Psychiatry Research-neuroimaging (2005)
Supervised Neural Gas with General Similarity Measure
Barbara Hammer;Marc Strickert;Thomas Villmann.
Neural Processing Letters (2005)
Batch and median neural gas
Marie Cottrell;Barbara Hammer;Alexander Hasenfuß;Thomas Villmann.
workshop on self-organizing maps (2006)
Neural maps and topographic vector quantization
H.-U. Bauer;M. Herrmann;T. Villmann.
Neural Networks (1999)
Vector Quantization by Optimal Neural Gas
M. Herrmann;Thomas Villmann.
international conference on artificial neural networks (1997)
Limited Rank Matrix Learning, discriminative dimension reduction and visualization
Kerstin Bunte;Petra Schneider;Barbara Hammer;Frank-Michael Schleif.
Neural Networks (2012)
If you think any of the details on this page are incorrect, let us know.
We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below: