Frank-Michael Schleif

According to our database, Frank-Michael Schleif authored at least 156 papers between 2004 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Crossing Domain Borders with Federated Few-Shot Adaptation.

Manuel Röder, Maximilian Münch, Christoph Raab, Frank-Michael Schleif

Proceedings of the 13th International Conference on Pattern Recognition Applications and Methods, 2024

2023

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

Maximilian Münch, Manuel Röder, Simon Heilig, Christoph Raab, Frank-Michael Schleif

Neurocomputing, October, 2023

Efficient Cross-Domain Federated Learning by MixStyle Approximation.

Manuel Röder, Leon Heller, Maximilian Münch, Frank-Michael Schleif

CoRR, 2023

How Important is the Temporal Context to Anticipate Oncoming Vehicles at Night?

Lukas Ewecker, Timo Winkler, Philipp Väth, Robin Schwager, Tim Brühl, Frank-Michael Schleif

Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics, 2023

Unlocking the Potential of Non-PSD Kernel Matrices: A Polar Decomposition-based Transformation for Improved Prediction Models.

Maximilian Münch, Manuel Röder, Frank-Michael Schleif

Proceedings of the 32nd ACM International Conference on Information and Knowledge Management, 2023

2022

Passive concept drift handling via variations of learning vector quantization.

Moritz Heusinger, Christoph Raab, Frank-Michael Schleif

Neural Comput. Appl., 2022

Domain adversarial tangent subspace alignment for explainable domain adaptation.

Christoph Raab, Manuel Röder, Frank-Michael Schleif

Neurocomputing, 2022

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

Luca Oneto, Nicolò Navarin, Frank-Michael Schleif

Neurocomputing, 2022

Dimensionality reduction in the context of dynamic social media data streams.

Moritz Heusinger, Christoph Raab, Frank-Michael Schleif

Evol. Syst., 2022

Memory Efficient Kernel Approximation for Non-Stationary and Indefinite Kernels.

Simon Heilig, Maximilian Münch, Frank-Michael Schleif

Proceedings of the International Joint Conference on Neural Networks, 2022

A Streaming Approach to the Core Vector Machine.

Moritz Heusinger, Frank-Michael Schleif

Proceedings of the Artificial Intelligence and Soft Computing, 2022

Adaptive multi-modal positive semi-definite and indefinite kernel fusion for binary classification.

Maximilian Münch, Christoph Raab, Simon Heilig, Manuel Röder, Frank-Michael Schleif

Proceedings of the 30th European Symposium on Artificial Neural Networks, 2022

2021

Revisiting Memory Efficient Kernel Approximation: An Indefinite Learning Perspective.

Simon Heilig, Maximilian Münch, Frank-Michael Schleif

CoRR, 2021

Scalable embedding of multiple perspectives for indefinite life-science data analysis.

Maximilian Münch, Simon Heilig, Philipp Väth, Frank-Michael Schleif

Proceedings of the IEEE Symposium Series on Computational Intelligence, 2021

Classification in Non-stationary Environments Using Coresets over Sliding Windows.

Moritz Heusinger, Frank-Michael Schleif

Proceedings of the Advances in Computational Intelligence, 2021

Domain Adversarial Tangent Learning Towards Interpretable Domain Adaptation.

Christoph Raab, Sascha Saralajew, Frank-Michael Schleif

Proceedings of the 29th European Symposium on Artificial Neural Networks, 2021

Multi-perspective embedding for non-metric time series classification.

Maximilian Münch, Simon Heilig, Frank-Michael Schleif

Proceedings of the 29th European Symposium on Artificial Neural Networks, 2021

Federated Learning - Methods, Applications and beyond.

Moritz Heusinger, Christoph Raab, Fabrice Rossi, Frank-Michael Schleif

Proceedings of the 29th European Symposium on Artificial Neural Networks, 2021

2020

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

Frank-Michael Schleif, Christoph Raab, Peter Tiño

Pattern Recognit. Lett., 2020

Transfer learning extensions for the probabilistic classification vector machine.

Christoph Raab, Frank-Michael Schleif

Neurocomputing, 2020

Reactive Soft Prototype Computing for Concept Drift Streams.

Christoph Raab, Moritz Heusinger, Frank-Michael Schleif

Neurocomputing, 2020

Data-Driven Supervised Learning for Life Science Data.

Maximilian Münch, Christoph Raab, Michael Biehl, Frank-Michael Schleif

Frontiers Appl. Math. Stat., 2020

Complex-Valued Embeddings of Generic Proximity Data.

Maximilian Münch, Michiel Straat, Michael Biehl, Frank-Michael Schleif

Proceedings of the Structural, Syntactic, and Statistical Pattern Recognition, 2020

Reactive Concept Drift Detection Using Coresets Over Sliding Windows.

Moritz Heusinger, Frank-Michael Schleif

Proceedings of the 2020 IEEE Symposium Series on Computational Intelligence, 2020

Low-Rank Subspace Override for Unsupervised Domain Adaptation.

Christoph Raab, Frank-Michael Schleif

Proceedings of the KI 2020: Advances in Artificial Intelligence, 2020

Encoding of Indefinite Proximity Data: A Structure Preserving Perspective.

Maximilian Münch, Christoph Raab, Frank-Michael Schleif

Proceedings of the Pattern Recognition Applications and Methods, 2020

Structure Preserving Encoding of Non-euclidean Similarity Data.

Maximilian Münch, Christoph Raab, Michael Biehl, Frank-Michael Schleif

Proceedings of the 9th International Conference on Pattern Recognition Applications and Methods, 2020

Random Projection in the Presence of Concept Drift in Supervised Environments.

Moritz Heusinger, Frank-Michael Schleif

Proceedings of the Artificial Intelligence and Soft Computing, 2020

Domain Invariant Representations with Deep Spectral Alignment.

Christoph Raab, Peter Meier, Frank-Michael Schleif

Proceedings of the 28th European Symposium on Artificial Neural Networks, 2020

Random Projection in supervised non-stationary environments.

Moritz Heusinger, Frank-Michael Schleif

Proceedings of the 28th European Symposium on Artificial Neural Networks, 2020

Analyzing Dynamic Social Media Data via Random Projection - A New Challenge for Stream Classifiers.

Moritz Heusinger, Christoph Raab, Frank-Michael Schleif

Proceedings of the 2020 IEEE Conference on Evolving and Adaptive Intelligent Systems, 2020

Bridging Adversarial and Statistical Domain Transfer via Spectral Adaptation Networks.

Christoph Raab, Philipp Väth, Peter Meier, Frank-Michael Schleif

Proceedings of the Computer Vision - ACCV 2020 - 15th Asian Conference on Computer Vision, Kyoto, Japan, November 30, 2020

2019

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

Luca Oneto, Kerstin Bunte, Frank-Michael Schleif

Neurocomputing, 2019

Globular cluster detection in the GAIA survey.

Mohammad Mohammadi, Nicolai Petkov, Kerstin Bunte, Reynier F. Peletier, Frank-Michael Schleif

Neurocomputing, 2019

Domain Adaptation via Low-Rank Basis Approximation.

Christoph Raab, Frank-Michael Schleif

CoRR, 2019

Passive Concept Drift Handling via Momentum Based Robust Soft Learning Vector Quantization.

Moritz Heusinger, Christoph Raab, Frank-Michael Schleif

Proceedings of the Advances in Self-Organizing Maps, Learning Vector Quantization, Clustering and Data Visualization, 2019

Device-Free Passive Human Counting with Bluetooth Low Energy Beacons.

Maximilian Münch, Frank-Michael Schleif

Proceedings of the Advances in Computational Intelligence, 2019

Reactive Soft Prototype Computing for frequent reoccurring Concept Drift.

Christoph Raab, Moritz Heusinger, Frank-Michael Schleif

Proceedings of the 27th European Symposium on Artificial Neural Networks, 2019

Towards a device-free passive presence detection system with Bluetooth Low Energy beacons.

Maximilian Münch, Karsten Huffstadt, Frank-Michael Schleif

Proceedings of the 27th European Symposium on Artificial Neural Networks, 2019

Recent trends in streaming data analysis, concept drift and analysis of dynamic data sets.

Albert Bifet, Barbara Hammer, Frank-Michael Schleif

Proceedings of the 27th European Symposium on Artificial Neural Networks, 2019

2018

Supervised low rank indefinite kernel approximation using minimum enclosing balls.

Frank-Michael Schleif, Andrej Gisbrecht, Peter Tiño

Neurocomputing, 2018

Sparsification of Indefinite Learning Models.

Frank-Michael Schleif, Christoph Raab, Peter Tiño

Proceedings of the Structural, Syntactic, and Statistical Pattern Recognition, 2018

Sparse Transfer Classification for Text Documents.

Christoph Raab, Frank-Michael Schleif

Proceedings of the KI 2018: Advances in Artificial Intelligence, 2018

Transfer learning for the probabilistic classification vector machine.

Christoph Raab, Frank-Michael Schleif

Proceedings of the 7th Symposium on Conformal and Probabilistic Prediction and Applications, 2018

2017

Indefinite Core Vector Machine.

Frank-Michael Schleif, Peter Tiño

Pattern Recognit., 2017

Small sets of random Fourier features by kernelized Matrix LVQ.

Frank-Michael Schleif

Proceedings of the 12th International Workshop on Self-Organizing Maps and Learning Vector Quantization, 2017

Indefinite Support Vector Regression.

Frank-Michael Schleif

Proceedings of the Artificial Neural Networks and Machine Learning - ICANN 2017, 2017

2016

Odor recognition in robotics applications by discriminative time-series modeling.

Frank-Michael Schleif, Barbara Hammer, Javier Gonzalez Monroy, Javier González Jiménez, José-Luis Blanco-Claraco, Michael Biehl, Nicolai Petkov

Pattern Anal. Appl., 2016

Probabilistic classifiers with low rank indefinite kernels.

Frank-Michael Schleif, Andrej Gisbrecht, Peter Tiño

CoRR, 2016

Low-Rank Kernel Space Representations in Prototype Learning.

Kerstin Bunte, Marika Kaden, Frank-Michael Schleif

Proceedings of the Advances in Self-Organizing Maps and Learning Vector Quantization, 2016

Learning in indefinite proximity spaces - recent trends.

Frank-Michael Schleif, Peter Tiño, Yingyu Liang

Proceedings of the 24th European Symposium on Artificial Neural Networks, 2016

Finding Small Sets of Random Fourier Features for Shift-Invariant Kernel Approximation.

Frank-Michael Schleif, Ata Kabán, Peter Tiño

Proceedings of the Artificial Neural Networks in Pattern Recognition, 2016

2015

Indefinite Proximity Learning: A Review.

Frank-Michael Schleif, Peter Tiño

Neural Comput., 2015

Generic probabilistic prototype based classification of vectorial and proximity data.

Frank-Michael Schleif

Neurocomputing, 2015

Metric learning for sequences in relational LVQ.

Bassam Mokbel, Benjamin Paaßen, Frank-Michael Schleif, Barbara Hammer

Neurocomputing, 2015

Metric and non-metric proximity transformations at linear costs.

Andrej Gisbrecht, Frank-Michael Schleif

Neurocomputing, 2015

Developments in computational intelligence and machine learning.

Michael Biehl, Alessandro Ghio, Frank-Michael Schleif

Neurocomputing, 2015

Sparse conformal prediction for dissimilarity data.

Frank-Michael Schleif, Xibin Zhu, Barbara Hammer

Ann. Math. Artif. Intell., 2015

Large Scale Indefinite Kernel Fisher Discriminant.

Frank-Michael Schleif, Andrej Gisbrecht, Peter Tiño

Proceedings of the Similarity-Based Pattern Recognition - Third International Workshop, 2015

Incremental probabilistic classification vector machine with linear costs.

Frank-Michael Schleif, H. Chen, Peter Tiño

Proceedings of the 2015 International Joint Conference on Neural Networks, 2015

Stationarity of Matrix Relevance LVQ.

Michael Biehl, Barbara Hammer, Frank-Michael Schleif, Petra Schneider, Thomas Villmann

Proceedings of the 2015 International Joint Conference on Neural Networks, 2015

Probabilistic Classification Vector Machine at large scale.

Frank-Michael Schleif, Andrej Gisbrecht, Peter Tiño

Proceedings of the 23rd European Symposium on Artificial Neural Networks, 2015

2014

Adaptive conformal semi-supervised vector quantization for dissimilarity data.

Xibin Zhu, Frank-Michael Schleif, Barbara Hammer

Pattern Recognit. Lett., 2014

Correlation-based embedding of pairwise score data.

Marc Strickert, Kerstin Bunte, Frank-Michael Schleif, Eyke Hüllermeier

Neurocomputing, 2014

Learning interpretable kernelized prototype-based models.

Daniela Hofmann, Frank-Michael Schleif, Benjamin Paaßen, Barbara Hammer

Neurocomputing, 2014

Learning vector quantization for (dis-)similarities.

Barbara Hammer, Daniela Hofmann, Frank-Michael Schleif, Xibin Zhu

Neurocomputing, 2014

Advances in artificial neural networks, machine learning, and computational intelligence (ESANN 2013).

Mark J. Embrechts, Fabrice Rossi, Frank-Michael Schleif, John Aldo Lee

Neurocomputing, 2014

Probabilistic Prototype Classification Using t-norms.

Tina Geweniger, Frank-Michael Schleif, Thomas Villmann

Proceedings of the Advances in Self-Organizing Maps and Learning Vector Quantization, 2014

High Dimensional Matrix Relevance Learning.

Frank-Michael Schleif, Thomas Villmann, Xibin Zhu

Proceedings of the 2014 IEEE International Conference on Data Mining Workshops, 2014

Discriminative Fast Soft Competitive Learning.

Frank-Michael Schleif

Proceedings of the Artificial Neural Networks and Machine Learning - ICANN 2014, 2014

Recent trends in learning of structured and non-standard data.

Frank-Michael Schleif, Peter Tiño, Thomas Villmann

Proceedings of the 22th European Symposium on Artificial Neural Networks, 2014

Proximity learning for non-standard big data.

Frank-Michael Schleif

Proceedings of the 22th European Symposium on Artificial Neural Networks, 2014

2013

Novel approaches in machine learning and computational intelligence.

Alessio Micheli, Frank-Michael Schleif, Peter Tiño

Neurocomputing, 2013

Data Analysis of (Non-)Metric Proximities at Linear Costs.

Frank-Michael Schleif, Andrej Gisbrecht

Proceedings of the Similarity-Based Pattern Recognition - Second International Workshop, 2013

Secure Semi-supervised Vector Quantization for Dissimilarity Data.

Xibin Zhu, Frank-Michael Schleif, Barbara Hammer

Proceedings of the Advances in Computational Intelligence, 2013

Sparse Prototype Representation by Core Sets.

Frank-Michael Schleif, Xibin Zhu, Barbara Hammer

Proceedings of the Intelligent Data Engineering and Automated Learning - IDEAL 2013, 2013

Semi-Supervised Vector Quantization for proximity data.

Xibin Zhu, Frank-Michael Schleif, Barbara Hammer

Proceedings of the 21st European Symposium on Artificial Neural Networks, 2013

2012

Limited Rank Matrix Learning, discriminative dimension reduction and visualization.

Kerstin Bunte, Petra Schneider, Barbara Hammer, Frank-Michael Schleif, Thomas Villmann, Michael Biehl

Neural Networks, 2012

Approximation techniques for clustering dissimilarity data.

Xibin Zhu, Andrej Gisbrecht, Frank-Michael Schleif, Barbara Hammer

Neurocomputing, 2012

Linear Time Relational Prototype Based Learning.

Andrej Gisbrecht, Bassam Mokbel, Frank-Michael Schleif, Xibin Zhu, Barbara Hammer

Int. J. Neural Syst., 2012

Patch Processing for Relational Learning Vector Quantization.

Xibin Zhu, Frank-Michael Schleif, Barbara Hammer

Proceedings of the Advances in Neural Networks - ISNN 2012, 2012

Relevance learning for short high-dimensional time series in the life sciences.

Frank-Michael Schleif, Andrej Gisbrecht, Barbara Hammer

Proceedings of the 2012 International Joint Conference on Neural Networks (IJCNN), 2012

Large margin linear discriminative visualization by Matrix Relevance Learning.

Michael Biehl, Kerstin Bunte, Frank-Michael Schleif, Petra Schneider, Thomas Villmann

Proceedings of the 2012 International Joint Conference on Neural Networks (IJCNN), 2012

A Conformal Classifier for Dissimilarity Data.

Frank-Michael Schleif, Xibin Zhu, Barbara Hammer

Proceedings of the Artificial Intelligence Applications and Innovations, 2012

Fast approximated relational and kernel clustering.

Frank-Michael Schleif, Xibin Zhu, Andrej Gisbrecht, Barbara Hammer

Proceedings of the 21st International Conference on Pattern Recognition, 2012

Learning Relevant Time Points for Time-Series Data in the Life Sciences.

Frank-Michael Schleif, Bassam Mokbel, Andrej Gisbrecht, Leslie Theunissen, Volker Dürr, Barbara Hammer

Proceedings of the Artificial Neural Networks and Machine Learning - ICANN 2012, 2012

White Box Classification of Dissimilarity Data.

Barbara Hammer, Bassam Mokbel, Frank-Michael Schleif, Xibin Zhu

Proceedings of the Hybrid Artificial Intelligent Systems - 7th International Conference, 2012

Adaptive learning for complex-valued data.

Kerstin Bunte, Frank-Michael Schleif, Michael Biehl

Proceedings of the 20th European Symposium on Artificial Neural Networks, 2012

Soft Competitive Learning for Large Data Sets.

Frank-Michael Schleif, Xibin Zhu, Barbara Hammer

Proceedings of the New Trends in Databases and Information Systems, 2012

2011

Divergence-based classification in learning vector quantization.

Ernest Mwebaze, Petra Schneider, Frank-Michael Schleif, Jennifer R. Aduwo, John A. Quinn, Sven Haase, Thomas Villmann, Michael Biehl

Neurocomputing, 2011

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

John Aldo Lee, Frank-Michael Schleif, Thomas Martinetz

Neurocomputing, 2011

Efficient Kernelized Prototype Based Classification.

Frank-Michael Schleif, Thomas Villmann, Barbara Hammer, Petra Schneider

Int. J. Neural Syst., 2011

Supervised learning of short and high-dimensional temporal sequences for life science measurements

Frank-Michael Schleif, Andrej Gisbrecht, Barbara Hammer

CoRR, 2011

Genetic algorithm for shift-uncertainty correction in 1-D NMR-based metabolite identifications and quantifications.

Frank-Michael Schleif, T. Riemer, U. Börner, L. Schnapka-Hille, Michael Cross

Bioinform., 2011

Topographic Mapping of Dissimilarity Data.

Barbara Hammer, Andrej Gisbrecht, Alexander Hasenfuss, Bassam Mokbel, Frank-Michael Schleif, Xibin Zhu

Proceedings of the Advances in Self-Organizing Maps - 8th International Workshop, 2011

Sparse kernelized vector quantization with local dependencies.

Frank-Michael Schleif

Proceedings of the 2011 International Joint Conference on Neural Networks, 2011

Linear Time Heuristics for Topographic Mapping of Dissimilarity Data.

Andrej Gisbrecht, Frank-Michael Schleif, Xibin Zhu, Barbara Hammer

Proceedings of the Intelligent Data Engineering and Automated Learning - IDEAL 2011, 2011

Prototype-Based Classification of Dissimilarity Data.

Barbara Hammer, Bassam Mokbel, Frank-Michael Schleif, Xibin Zhu

Proceedings of the Advances in Intelligent Data Analysis X - 10th International Symposium, 2011

Relational Extensions of Learning Vector Quantization.

Barbara Hammer, Frank-Michael Schleif, Xibin Zhu

Proceedings of the Neural Information Processing - 18th International Conference, 2011

Accelerating Kernel Neural Gas.

Frank-Michael Schleif, Andrej Gisbrecht, Barbara Hammer

Proceedings of the Artificial Neural Networks and Machine Learning - ICANN 2011, 2011

Recent trends in computational intelligence in life sciences.

Udo Seiffert, Frank-Michael Schleif, Dietlind Zühlke

Proceedings of the 19th European Symposium on Artificial Neural Networks, 2011

Multivariate class labeling in Robust Soft LVQ.

Petra Schneider, Tina Geweniger, Frank-Michael Schleif, Michael Biehl, Thomas Villmann

Proceedings of the 19th European Symposium on Artificial Neural Networks, 2011

Mathematical Foundations of the Self Organized Neighbor Embedding (SONE) for Dimension Reduction and Visualization.

Kerstin Bunte, Frank-Michael Schleif, Sven Haase, Thomas Villmann

Proceedings of the 19th European Symposium on Artificial Neural Networks, 2011

Accelerating kernel clustering for biomedical data analysis.

Andrej Gisbrecht, Barbara Hammer, Frank-Michael Schleif, Xibin Zhu

Proceedings of the 2011 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, 2011

2010

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

Stephan Simmuteit, Frank-Michael Schleif, Thomas Villmann, Barbara Hammer

Knowl. Inf. Syst., 2010

Advances in computational intelligence and learning (ESANN 2009).

Cecilio Angulo, John Aldo Lee, Frank-Michael Schleif

Neurocomputing, 2010

Generalized Derivative Based Kernelized Learning Vector Quantization.

Frank-Michael Schleif, Thomas Villmann, Barbara Hammer, Petra Schneider, Michael Biehl

Proceedings of the Intelligent Data Engineering and Automated Learning, 2010

Divergence Based Online Learning in Vector Quantization.

Thomas Villmann, Sven Haase, Frank-Michael Schleif, Barbara Hammer

Proceedings of the Artificial Intelligence and Soft Computing, 2010

Learning vector quantization for heterogeneous structured data.

Dietlind Zühlke, Frank-Michael Schleif, Tina Geweniger, Sven Haase, Thomas Villmann

Proceedings of the 18th European Symposium on Artificial Neural Networks, 2010

Sparse representation of data.

Thomas Villmann, Frank-Michael Schleif, Barbara Hammer

Proceedings of the 18th European Symposium on Artificial Neural Networks, 2010

Divergence based Learning Vector Quantization.

Ernest Mwebaze, Petra Schneider, Frank-Michael Schleif, Sven Haase, Thomas Villmann, Michael Biehl

Proceedings of the 18th European Symposium on Artificial Neural Networks, 2010

The Mathematics of Divergence Based Online Learning in Vector Quantization.

Thomas Villmann, Sven Haase, Frank-Michael Schleif, Barbara Hammer, Michael Biehl

Proceedings of the Artificial Neural Networks in Pattern Recognition, 2010

2009

Prototype Based Classification in Bioinformatics.

Frank-Michael Schleif, Thomas Villmann, Barbara Hammer

Proceedings of the Encyclopedia of Artificial Intelligence (3 Volumes), 2009

Supervised data analysis and reliability estimation with exemplary application for spectral data.

Frank-Michael Schleif, Thomas Villmann, Matthias Ongyerth

Neurocomputing, 2009

Advances in machine learning and computational intelligence.

Frank-Michael Schleif, Michael Biehl, Alfredo Vellido

Neurocomputing, 2009

Cancer informatics by prototype networks in mass spectrometry.

Frank-Michael Schleif, Thomas Villmann, Markus Kostrzewa, Barbara Hammer, Alexander Gammerman

Artif. Intell. Medicine, 2009

Hierarchical PCA Using Tree-SOM for the Identification of Bacteria.

Stephan Simmuteit, Frank-Michael Schleif, Thomas Villmann, Markus Kostrzewa

Proceedings of the Advances in Self-Organizing Maps, 7th International Workshop, 2009

Funtional vector quantization by neural maps.

Thomas Villmann, Frank-Michael Schleif

Proceedings of the First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, 2009

Matrix Metric Adaptation Linear Discriminant Analysis of Biomedical Data.

Marc Strickert, Jens Keilwagen, Frank-Michael Schleif, Thomas Villmann, Michael Biehl

Proceedings of the Bio-Inspired Systems: Computational and Ambient Intelligence, 2009

Tanimoto Metric in Tree-SOM for Improved Representation of Mass Spectrometry Data with an Underlying Taxonomic Structure.

Stephan Simmuteit, Frank-Michael Schleif, Thomas Villmann, Thomas Elssner

Proceedings of the International Conference on Machine Learning and Applications, 2009

Neural Maps and Learning Vector Quantization - Theory and Applications.

Frank-Michael Schleif, Thomas Villmann

Proceedings of the 17th European Symposium on Artificial Neural Networks, 2009

Unleashing Pearson Correlation for Faithful Analysis of Biomedical Data.

Marc Strickert, Frank-Michael Schleif, Thomas Villmann, Udo Seiffert

Proceedings of the Similarity-Based Clustering, 2009

2008

Analysis of Spectral Data in Clinical Proteomics by Use of Learning Vector Quantizers.

Frank-Michael Schleif, Thomas Villmann, Barbara Hammer, Martijn van der Werff, André M. Deelder, Rob A. E. M. Tollenaar

Proceedings of the Computational Intelligence in Biomedicine and Bioinformatics, 2008

Fuzzy classification using information theoretic learning vector quantization.

Thomas Villmann, Barbara Hammer, Frank-Michael Schleif, Wieland Hermann, Marie Cottrell

Neurocomputing, 2008

Prototype based fuzzy classification in clinical proteomics.

Frank-Michael Schleif, Thomas Villmann, Barbara Hammer

Int. J. Approx. Reason., 2008

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

Thomas Villmann, Frank-Michael Schleif, Markus Kostrzewa, Axel Walch, Barbara Hammer

Briefings Bioinform., 2008

Derivatives of Pearson Correlation for Gradient-based Analysis of Biomedical Data.

Marc Strickert, Frank-Michael Schleif, Udo Seiffert, Thomas Villmann

Inteligencia Artif., 2008

Comparison of Cluster Algorithms for the Analysis of Text Data Using Kolmogorov Complexity.

Tina Geweniger, Frank-Michael Schleif, Alexander Hasenfuss, Barbara Hammer, Thomas Villmann

Proceedings of the Advances in Neuro-Information Processing, 15th International Conference, 2008

Metric adaptation for supervised attribute rating.

Marc Strickert, Frank-Michael Schleif, Thomas Villmann

Proceedings of the 16th European Symposium on Artificial Neural Networks, 2008

Generalized matrix learning vector quantizer for the analysis of spectral data.

Petra Schneider, Frank-Michael Schleif, Thomas Villmann, Michael Biehl

Proceedings of the 16th European Symposium on Artificial Neural Networks, 2008

Sparse Coding Neural Gas for Analysis of Nuclear Magnetic Resonance Spectroscopy.

Frank-Michael Schleif, Matthias Ongyerth, Thomas Villmann

Proceedings of the Twenty-First IEEE International Symposium on Computer-Based Medical Systems, 2008

2007

Maschinelles Lernen mit Prototypmethoden in der klinischen Proteomik.

Frank-Michael Schleif

Künstliche Intell., 2007

Margin-based active learning for LVQ networks.

Frank-Michael Schleif, Barbara Hammer, Thomas Villmann

Neurocomputing, 2007

Analysis of Proteomic Spectral Data by Multi Resolution Analysis and Self-Organizing Maps.

Frank-Michael Schleif, Thomas Villmann, Barbara Hammer

Proceedings of the Applications of Fuzzy Sets Theory, 2007

Fuzzy Labeled Self-Organizing Map for Classification of Spectra.

Thomas Villmann, Frank-Michael Schleif, Erzsébet Merényi, Barbara Hammer

Proceedings of the Computational and Ambient Intelligence, 2007

Supervised Neural Gas for Classification of Functional Data and Its Application to the Analysis of Clinical Proteom Spectra.

Frank-Michael Schleif, Thomas Villmann, Barbara Hammer

Proceedings of the Computational and Ambient Intelligence, 2007

Neural Gas Clustering for Dissimilarity Data with Continuous Prototypes.

Alexander Hasenfuss, Barbara Hammer, Frank-Michael Schleif, Thomas Villmann

Proceedings of the Computational and Ambient Intelligence, 2007

Intuitive Clustering of Biological Data.

Barbara Hammer, Alexander Hasenfuss, Frank-Michael Schleif, Thomas Villmann, Marc Strickert, Udo Seiffert

Proceedings of the International Joint Conference on Neural Networks, 2007

Association Learning in SOMs for Fuzzy-Classification.

Thomas Villmann, Frank-Michael Schleif, Martijn van der Werff, André M. Deelder, Rob A. E. M. Tollenaar

Proceedings of the Sixth International Conference on Machine Learning and Applications, 2007

Visualization of Fuzzy Information in Fuzzy-Classification for Image Segmentation using MDS.

Thomas Villmann, Marc Strickert, Cornelia Brüß, Frank-Michael Schleif, Udo Seiffert

Proceedings of the 15th European Symposium on Artificial Neural Networks, 2007

Advances in pre-processing and model generation for mass spectrometric data analysis.

Frank-Michael Schleif

Proceedings of the Similarity-based Clustering and its Application to Medicine and Biology, 25.03., 2007

Statistical Classification and Visualization of MALDI-Imaging Data.

Marc Gerhard, Soren-Oliver Deininger, Frank-Michael Schleif

Proceedings of the 20th IEEE International Symposium on Computer-Based Medical Systems (CBMS 2007), 2007

2006

Comparison of relevance learning vector quantization with other metric adaptive classification methods.

Thomas Villmann, Frank-Michael Schleif, Barbara Hammer

Neural Networks, 2006

Fuzzy classification by fuzzy labeled neural gas.

Thomas Villmann, Barbara Hammer, Frank-Michael Schleif, Tina Geweniger, Wieland Hermann

Neural Networks, 2006

Prototype-based fuzzy classification with local relevance for proteomics.

Thomas Villmann, Frank-Michael Schleif, Barbara Hammer

Neurocomputing, 2006

Prototype Based Classification Using Information Theoretic Learning.

Thomas Villmann, Barbara Hammer, Frank-Michael Schleif, Tina Geweniger, Tom Fischer, Marie Cottrell

Proceedings of the Neural Information Processing, 13th International Conference, 2006

Learning Vector Quantization Classification with Local Relevance Determination for Medical Data.

Barbara Hammer, Thomas Villmann, Frank-Michael Schleif, Cornelia Albani, Wieland Hermann

Proceedings of the Artificial Intelligence and Soft Computing, 2006

Prototype based machine learning for clinical proteomics.

Frank-Michael Schleif

Proceedings of the Ausgezeichnete Informatikdissertationen 2006, 2006

Fuzzy image segmentation with Fuzzy Labelled Neural Gas.

Cornelia Brüß, Felix Bollenbeck, Frank-Michael Schleif, Winfriede Weschke, Thomas Villmann, Udo Seiffert

Proceedings of the 14th European Symposium on Artificial Neural Networks, 2006

Analysis and Visualization of Proteomic Data by Fuzzy Labeled Self-Organizing Maps.

Frank-Michael Schleif, Thomas Elssner, Markus Kostrzewa, Thomas Villmann, Barbara Hammer

Proceedings of the 19th IEEE International Symposium on Computer-Based Medical Systems (CBMS 2006), 2006

Fuzzy Labeled Self-Organizing Map with Label-Adjusted Prototypes.

Thomas Villmann, Udo Seiffert, Frank-Michael Schleif, Cornelia Brüß, Tina Geweniger, Barbara Hammer

Proceedings of the Artificial Neural Networks in Pattern Recognition, Second IAPR Workshop, 2006

Supervised Batch Neural Gas.

Barbara Hammer, Alexander Hasenfuss, Frank-Michael Schleif, Thomas Villmann

Proceedings of the Artificial Neural Networks in Pattern Recognition, Second IAPR Workshop, 2006

Prototype based machine learning for clinical proteomics.

Frank-Michael Schleif

PhD thesis, 2006

2005

Local Metric Adaptation for Soft Nearest Prototype Classification to Classify Proteomic Data.

Frank-Michael Schleif, Thomas Villmann, Barbara Hammer

Proceedings of the Fuzzy Logic and Applications, 6th International Workshop, 2005

Fuzzy Labeled Soft Nearest Neighbor Classification with Relevance Learning.

Thomas Villmann, Frank-Michael Schleif, Barbara Hammer

Proceedings of the Fourth International Conference on Machine Learning and Applications, 2005

2004

Supervised relevance neural gas and unified maximum separability analysis for classification of mass spectrometric data.

Frank-Michael Schleif, U. Clauss, Thomas Villmann, Barbara Hammer

Proceedings of the 2004 International Conference on Machine Learning and Applications, 2004