Frank-Michael Schleif

179 papers A 1B 45C 11Misc 6Journal 62Unranked 48

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Learning in federated and dynamic environments: A tutorial on challenges, trends, and practical strategies. Neurocomputing Mirko Polato, Barbara Hammer, Manuel Röder, Frank-Michael Schleif
2026	J	`jnl`	The evolution of resource-aware cooperation in federated learning. Neurocomputing Manuel Röder, Gengcheng Lyu, Fabian Geiger, Frank-Michael Schleif
2025	—	`conf`	Contextualized Segmentation of Milling Processes Using Discrete Rule-Based Pattern Recognition. EANN (2) Lukas Klehr, Bastian Engelmann, Frank-Michael Schleif, Daniel Regulin
2025	J	`jnl`	Domain Borders Are There to Be Crossed With Federated Few-Shot Adaptation. CoRR Manuel Röder, Christoph Raab, Frank-Michael Schleif
2025	B	`conf`	Driving Cooperation in Federated Learning via Evolutionary Game Theory. IJCNN Manuel Röder, Fabian Geiger, Frank-Michael Schleif
2025	—	`conf`	On the Use of Smooth-L1 Approximation in Echo State Networks for Sparse and Efficient Temporal Modeling. IDEAL (1) Gengcheng Lyu, Manuel Röder, Frank-Michael Schleif
2024	—	`ed.`	Advances in Self-Organizing Maps, Learning Vector Quantization, Interpretable Machine Learning, and Beyond - Proceedings of the 15th International Workshop, WSOM+ 2024, Mittweida, Germany, July 10-12, 2024 WSOM+ Thomas Villmann, Marika Kaden, Tina Geweniger, Frank-Michael Schleif
2024	C	`conf`	Crossing Domain Borders with Federated Few-Shot Adaptation. ICPRAM Manuel Röder, Maximilian Münch, Christoph Raab, Frank-Michael Schleif
2024	—	`conf`	Deep Transfer Hashing for Adaptive Learning on Federated Streaming Data. IAL@PKDD/ECML Manuel Röder, Frank-Michael Schleif
2024	J	`jnl`	Deep Transfer Hashing for Adaptive Learning on Federated Streaming Data. CoRR Manuel Röder, Frank-Michael Schleif
2024	B	`conf`	Machine learning in distributed, federated and non-stationary environments - recent trends. ESANN Mirko Polato, Barbara Hammer, Frank-Michael Schleif
2024	—	`conf`	Optimizing YOLOv5 for Green AI: A Study on Model Pruning and Lightweight Networks. WSOM+ Bangguo Xu, Simei Yan, Liang Liu, Frank-Michael Schleif
2024	—	`conf`	Practical Approaches to Approximate Dominant Eigenvalues in Large Matrices. WSOM+ Frank-Michael Schleif
2024	B	`conf`	Sparse Uncertainty-Informed Sampling from Federated Streaming Data. ESANN Manuel Röder, Frank-Michael Schleif
2024	J	`jnl`	Sparse Uncertainty-Informed Sampling from Federated Streaming Data. CoRR Manuel Röder, Frank-Michael Schleif
2023	J	`jnl`	Efficient Cross-Domain Federated Learning by MixStyle Approximation. CoRR Manuel Röder, Leon Heller, Maximilian Münch, Frank-Michael Schleif
2023	B	`conf`	How Important is the Temporal Context to Anticipate Oncoming Vehicles at Night? SMC Lukas Ewecker, Timo Winkler, Philipp Väth, Robin Schwager, Tim Brühl, Frank-Michael Schleif
2023	B	`conf`	Sparse Nyström Approximation for Non-Vectorial Data Using Class-informed Landmark Selection. ESANN Maximilian Münch, Katrin Sophie Bohnsack, Alexander Engelsberger, Frank-Michael Schleif, Thomas Villmann
2023	J	`jnl`	Static and adaptive subspace information fusion for indefinite heterogeneous proximity data. Neurocomputing Maximilian Münch, Manuel Röder, Simon Heilig, Christoph Raab, Frank-Michael Schleif
2023	A	`conf`	Unlocking the Potential of Non-PSD Kernel Matrices: A Polar Decomposition-based Transformation for Improved Prediction Models. CIKM Maximilian Münch, Manuel Röder, Frank-Michael Schleif
2022	—	`conf`	A Streaming Approach to the Core Vector Machine. ICAISC (2) Moritz Heusinger, Frank-Michael Schleif
2022	B	`conf`	Adaptive multi-modal positive semi-definite and indefinite kernel fusion for binary classification. ESANN Maximilian Münch, Christoph Raab, Simon Heilig, Manuel Röder, Frank-Michael Schleif
2022	J	`jnl`	Advances in artificial neural networks, machine learning and computational intelligence. Neurocomputing Luca Oneto, Nicolò Navarin, Frank-Michael Schleif
2022	J	`jnl`	Dimensionality reduction in the context of dynamic social media data streams. Evol. Syst. Moritz Heusinger, Christoph Raab, Frank-Michael Schleif
2022	J	`jnl`	Domain adversarial tangent subspace alignment for explainable domain adaptation. Neurocomputing Christoph Raab, Manuel Röder, Frank-Michael Schleif
2022	J	`jnl`	Federated Learning - Methods, Applications and beyond. CoRR Moritz Heusinger, Christoph Raab, Fabrice Rossi, Frank-Michael Schleif
2022	B	`conf`	Memory Efficient Kernel Approximation for Non-Stationary and Indefinite Kernels. IJCNN Simon Heilig, Maximilian Münch, Frank-Michael Schleif
2022	J	`jnl`	Passive concept drift handling via variations of learning vector quantization. Neural Comput. Appl. Moritz Heusinger, Christoph Raab, Frank-Michael Schleif
2021	—	`conf`	Classification in Non-stationary Environments Using Coresets over Sliding Windows. IWANN (1) Moritz Heusinger, Frank-Michael Schleif
2021	B	`conf`	Domain Adversarial Tangent Learning Towards Interpretable Domain Adaptation. ESANN Christoph Raab, Sascha Saralajew, Frank-Michael Schleif
2021	B	`conf`	Federated Learning - Methods, Applications and beyond. ESANN Moritz Heusinger, Christoph Raab, Fabrice Rossi, Frank-Michael Schleif
2021	B	`conf`	Multi-perspective embedding for non-metric time series classification. ESANN Maximilian Münch, Simon Heilig, Frank-Michael Schleif
2021	J	`jnl`	Revisiting Memory Efficient Kernel Approximation: An Indefinite Learning Perspective. CoRR Simon Heilig, Maximilian Münch, Frank-Michael Schleif
2021	—	`conf`	Scalable embedding of multiple perspectives for indefinite life-science data analysis. SSCI Maximilian Münch, Simon Heilig, Philipp Väth, Frank-Michael Schleif
2020	—	`conf`	Analyzing Dynamic Social Media Data via Random Projection - A New Challenge for Stream Classifiers. EAIS Moritz Heusinger, Christoph Raab, Frank-Michael Schleif
2020	—	`conf`	Bridging Adversarial and Statistical Domain Transfer via Spectral Adaptation Networks. ACCV (3) Christoph Raab, Philipp Väth, Peter Meier, Frank-Michael Schleif
2020	—	`conf`	Complex-Valued Embeddings of Generic Proximity Data. S+SSPR Maximilian Münch, Michiel Straat, Michael Biehl, Frank-Michael Schleif
2020	J	`jnl`	Complex-valued embeddings of generic proximity data. CoRR Maximilian Münch, Michiel Straat, Michael Biehl, Frank-Michael Schleif
2020	J	`jnl`	Data-Driven Supervised Learning for Life Science Data. Frontiers Appl. Math. Stat. Maximilian Münch, Christoph Raab, Michael Biehl, Frank-Michael Schleif
2020	B	`conf`	Domain Invariant Representations with Deep Spectral Alignment. ESANN Christoph Raab, Peter Meier, Frank-Michael Schleif
2020	—	`conf`	Encoding of Indefinite Proximity Data: A Structure Preserving Perspective. ICPRAM (Revised Selected Papers) Maximilian Münch, Christoph Raab, Frank-Michael Schleif
2020	Misc	`conf`	Low-Rank Subspace Override for Unsupervised Domain Adaptation. KI Christoph Raab, Frank-Michael Schleif
2020	B	`conf`	Random Projection in supervised non-stationary environments. ESANN Moritz Heusinger, Frank-Michael Schleif
2020	—	`conf`	Random Projection in the Presence of Concept Drift in Supervised Environments. ICAISC (1) Moritz Heusinger, Frank-Michael Schleif
2020	—	`conf`	Reactive Concept Drift Detection Using Coresets Over Sliding Windows. SSCI Moritz Heusinger, Frank-Michael Schleif
2020	J	`jnl`	Reactive Soft Prototype Computing for Concept Drift Streams. CoRR Christoph Raab, Moritz Heusinger, Frank-Michael Schleif
2020	J	`jnl`	Reactive Soft Prototype Computing for Concept Drift Streams. Neurocomputing Christoph Raab, Moritz Heusinger, Frank-Michael Schleif
2020	J	`jnl`	Sparsification of core set models in non-metric supervised learning. Pattern Recognit. Lett. Frank-Michael Schleif, Christoph Raab, Peter Tiño
2020	C	`conf`	Structure Preserving Encoding of Non-euclidean Similarity Data. ICPRAM Maximilian Münch, Christoph Raab, Michael Biehl, Frank-Michael Schleif
2020	J	`jnl`	Transfer learning extensions for the probabilistic classification vector machine. CoRR Christoph Raab, Frank-Michael Schleif
2020	J	`jnl`	Transfer learning extensions for the probabilistic classification vector machine. Neurocomputing Christoph Raab, Frank-Michael Schleif
2019	J	`jnl`	Advances in artificial neural networks, machine learning and computational intelligence: Selected papers from the 26 Neurocomputing Luca Oneto, Kerstin Bunte, Frank-Michael Schleif
2019	—	`conf`	Device-Free Passive Human Counting with Bluetooth Low Energy Beacons. IWANN (2) Maximilian Münch, Frank-Michael Schleif
2019	J	`jnl`	Domain Adaptation via Low-Rank Basis Approximation. CoRR Christoph Raab, Frank-Michael Schleif
2019	J	`jnl`	Globular cluster detection in the GAIA survey. Neurocomputing Mohammad Mohammadi, Nicolai Petkov, Kerstin Bunte, Reynier F. Peletier, Frank-Michael Schleif
2019	—	`conf`	Passive Concept Drift Handling via Momentum Based Robust Soft Learning Vector Quantization. WSOM+ Moritz Heusinger, Christoph Raab, Frank-Michael Schleif
2019	B	`conf`	Reactive Soft Prototype Computing for frequent reoccurring Concept Drift. ESANN Christoph Raab, Moritz Heusinger, Frank-Michael Schleif
2019	B	`conf`	Recent trends in streaming data analysis, concept drift and analysis of dynamic data sets. ESANN Albert Bifet, Barbara Hammer, Frank-Michael Schleif
2019	B	`conf`	Towards a device-free passive presence detection system with Bluetooth Low Energy beacons. ESANN Maximilian Münch, Karsten Huffstadt, Frank-Michael Schleif
2018	B	`conf`	Globular Cluster Detection in the Gaia Survey. ESANN Mohammad Mohammadi, Reynier Peletier, Frank-Michael Schleif, Nicolai Petkov, Kerstin Bunte
2018	Misc	`conf`	Sparse Transfer Classification for Text Documents. KI Christoph Raab, Frank-Michael Schleif
2018	—	`conf`	Sparsification of Indefinite Learning Models. S+SSPR Frank-Michael Schleif, Christoph Raab, Peter Tiño
2018	J	`jnl`	Supervised low rank indefinite kernel approximation using minimum enclosing balls. Neurocomputing Frank-Michael Schleif, Andrej Gisbrecht, Peter Tiño
2018	—	`conf`	Transfer learning for the probabilistic classification vector machine. COPA Christoph Raab, Frank-Michael Schleif
2017	J	`jnl`	Indefinite Core Vector Machine. Pattern Recognit. Frank-Michael Schleif, Peter Tiño
2017	—	`conf`	Indefinite Support Vector Regression. ICANN (2) Frank-Michael Schleif
2017	—	`conf`	Small sets of random Fourier features by kernelized Matrix LVQ. WSOM Frank-Michael Schleif
2016	—	`conf`	Finding Small Sets of Random Fourier Features for Shift-Invariant Kernel Approximation. ANNPR Frank-Michael Schleif, Ata Kabán, Peter Tiño
2016	B	`conf`	Learning in indefinite proximity spaces - recent trends. ESANN Frank-Michael Schleif, Peter Tiño, Yingyu Liang
2016	—	`conf`	Low-Rank Kernel Space Representations in Prototype Learning. WSOM Kerstin Bunte, Marika Kaden, Frank-Michael Schleif
2016	J	`jnl`	Odor recognition in robotics applications by discriminative time-series modeling. Pattern Anal. Appl. Frank-Michael Schleif, Barbara Hammer, Javier Gonzalez Monroy, Javier González Jiménez, José Luis Blanco-Claraco, Michael Biehl, Nicolai Petkov
2016	J	`jnl`	Probabilistic classifiers with low rank indefinite kernels. CoRR Frank-Michael Schleif, Andrej Gisbrecht, Peter Tiño
2015	J	`jnl`	Developments in computational intelligence and machine learning. Neurocomputing Michael Biehl, Alessandro Ghio, Frank-Michael Schleif
2015	J	`jnl`	Generic probabilistic prototype based classification of vectorial and proximity data. Neurocomputing Frank-Michael Schleif
2015	B	`conf`	Incremental probabilistic classification vector machine with linear costs. IJCNN Frank-Michael Schleif, H. Chen, Peter Tiño
2015	J	`jnl`	Indefinite Proximity Learning: A Review. Neural Comput. Frank-Michael Schleif, Peter Tiño
2015	—	`conf`	Large Scale Indefinite Kernel Fisher Discriminant. SIMBAD Frank-Michael Schleif, Andrej Gisbrecht, Peter Tiño
2015	J	`jnl`	Metric and non-metric proximity transformations at linear costs. Neurocomputing Andrej Gisbrecht, Frank-Michael Schleif
2015	J	`jnl`	Metric learning for sequences in relational LVQ. Neurocomputing Bassam Mokbel, Benjamin Paaßen, Frank-Michael Schleif, Barbara Hammer
2015	B	`conf`	Probabilistic Classification Vector Machine at large scale. ESANN Frank-Michael Schleif, Andrej Gisbrecht, Peter Tiño
2015	J	`jnl`	Sparse conformal prediction for dissimilarity data. Ann. Math. Artif. Intell. Frank-Michael Schleif, Xibin Zhu, Barbara Hammer
2015	B	`conf`	Stationarity of Matrix Relevance LVQ. IJCNN Michael Biehl, Barbara Hammer, Frank-Michael Schleif, Petra Schneider, Thomas Villmann
2014	J	`jnl`	Adaptive conformal semi-supervised vector quantization for dissimilarity data. Pattern Recognit. Lett. Xibin Zhu, Frank-Michael Schleif, Barbara Hammer
2014	—	`ed.`	Advances in Self-Organizing Maps and Learning Vector Quantization - Proceedings of the 10th International Workshop, WSOM 2014, Mittweida, Germany, July, 2-4, 2014 WSOM Thomas Villmann, Frank-Michael Schleif, Marika Kaden, Mandy Lange
2014	J	`jnl`	Advances in artificial neural networks, machine learning, and computational intelligence (ESANN 2013). Neurocomputing Mark J. Embrechts, Fabrice Rossi, Frank-Michael Schleif, John Aldo Lee
2014	J	`jnl`	Correlation-based embedding of pairwise score data. Neurocomputing Marc Strickert, Kerstin Bunte, Frank-Michael Schleif, Eyke Hüllermeier
2014	C	`conf`	Discriminative Fast Soft Competitive Learning. ICANN Frank-Michael Schleif
2014	—	`conf`	High Dimensional Matrix Relevance Learning. ICDM Workshops Frank-Michael Schleif, Thomas Villmann, Xibin Zhu
2014	J	`jnl`	Learning interpretable kernelized prototype-based models. Neurocomputing Daniela Hofmann, Frank-Michael Schleif, Benjamin Paaßen, Barbara Hammer
2014	J	`jnl`	Learning vector quantization for (dis-)similarities. Neurocomputing Barbara Hammer, Daniela Hofmann, Frank-Michael Schleif, Xibin Zhu
2014	J	`jnl`	Metric and non-metric proximity transformations at linear costs. CoRR Andrej Gisbrecht, Frank-Michael Schleif
2014	—	`conf`	Probabilistic Prototype Classification Using t-norms. WSOM Tina Geweniger, Frank-Michael Schleif, Thomas Villmann
2014	B	`conf`	Proximity learning for non-standard big data. ESANN Frank-Michael Schleif
2014	B	`conf`	Recent trends in learning of structured and non-standard data. ESANN Frank-Michael Schleif, Peter Tiño, Thomas Villmann
2013	—	`conf`	Data Analysis of (Non-)Metric Proximities at Linear Costs. SIMBAD Frank-Michael Schleif, Andrej Gisbrecht
2013	J	`jnl`	Novel approaches in machine learning and computational intelligence. Neurocomputing Alessio Micheli, Frank-Michael Schleif, Peter Tiño
2013	—	`conf`	Secure Semi-supervised Vector Quantization for Dissimilarity Data. IWANN (1) Xibin Zhu, Frank-Michael Schleif, Barbara Hammer
2013	B	`conf`	Semi-Supervised Vector Quantization for proximity data. ESANN Xibin Zhu, Frank-Michael Schleif, Barbara Hammer
2013	C	`conf`	Sparse Prototype Representation by Core Sets. IDEAL Frank-Michael Schleif, Xibin Zhu, Barbara Hammer
2012	—	`conf`	A Conformal Classifier for Dissimilarity Data. AIAI (2) Frank-Michael Schleif, Xibin Zhu, Barbara Hammer
2012	B	`conf`	Adaptive learning for complex-valued data. ESANN Kerstin Bunte, Frank-Michael Schleif, Michael Biehl
2012	J	`jnl`	Approximation techniques for clustering dissimilarity data. Neurocomputing Xibin Zhu, Andrej Gisbrecht, Frank-Michael Schleif, Barbara Hammer
2012	B	`conf`	Fast approximated relational and kernel clustering. ICPR Frank-Michael Schleif, Xibin Zhu, Andrej Gisbrecht, Barbara Hammer
2012	B	`conf`	Large margin linear discriminative visualization by Matrix Relevance Learning. IJCNN Michael Biehl, Kerstin Bunte, Frank-Michael Schleif, Petra Schneider, Thomas Villmann
2012	—	`conf`	Learning Relevant Time Points for Time-Series Data in the Life Sciences. ICANN (2) Frank-Michael Schleif, Bassam Mokbel, Andrej Gisbrecht, Leslie Theunissen, Volker Dürr, Barbara Hammer
2012	J	`jnl`	Limited Rank Matrix Learning, discriminative dimension reduction and visualization. Neural Networks Kerstin Bunte, Petra Schneider, Barbara Hammer, Frank-Michael Schleif, Thomas Villmann, Michael Biehl
2012	J	`jnl`	Linear Time Relational Prototype Based Learning. Int. J. Neural Syst. Andrej Gisbrecht, Bassam Mokbel, Frank-Michael Schleif, Xibin Zhu, Barbara Hammer
2012	—	`conf`	Patch Processing for Relational Learning Vector Quantization. ISNN (1) Xibin Zhu, Frank-Michael Schleif, Barbara Hammer
2012	B	`conf`	Relevance learning for short high-dimensional time series in the life sciences. IJCNN Frank-Michael Schleif, Andrej Gisbrecht, Barbara Hammer
2012	—	`conf`	Soft Competitive Learning for Large Data Sets. ADBIS Workshops Frank-Michael Schleif, Xibin Zhu, Barbara Hammer
2012	—	`conf`	White Box Classification of Dissimilarity Data. HAIS (1) Barbara Hammer, Bassam Mokbel, Frank-Michael Schleif, Xibin Zhu
2011	—	`conf`	Accelerating Kernel Neural Gas. ICANN (1) Frank-Michael Schleif, Andrej Gisbrecht, Barbara Hammer
2011	C	`conf`	Accelerating kernel clustering for biomedical data analysis. CIBCB Andrej Gisbrecht, Barbara Hammer, Frank-Michael Schleif, Xibin Zhu
2011	J	`jnl`	Advances in artificial neural networks, machine learning, and computational intelligence. Neurocomputing John Aldo Lee, Frank-Michael Schleif, Thomas Martinetz
2011	J	`jnl`	Divergence-based classification in learning vector quantization. Neurocomputing Ernest Mwebaze, Petra Schneider, Frank-Michael Schleif, Jennifer R. Aduwo, John A. Quinn, Sven Haase, Thomas Villmann, Michael Biehl
2011	J	`jnl`	Efficient Kernelized Prototype Based Classification. Int. J. Neural Syst. Frank-Michael Schleif, Thomas Villmann, Barbara Hammer, Petra Schneider
2011	J	`jnl`	Genetic algorithm for shift-uncertainty correction in 1-D NMR-based metabolite identifications and quantifications. Bioinform. Frank-Michael Schleif, T. Riemer, U. Börner, L. Schnapka-Hille, Michael Cross
2011	C	`conf`	Linear Time Heuristics for Topographic Mapping of Dissimilarity Data. IDEAL Andrej Gisbrecht, Frank-Michael Schleif, Xibin Zhu, Barbara Hammer
2011	B	`conf`	Mathematical Foundations of the Self Organized Neighbor Embedding (SONE) for Dimension Reduction and Visualization. ESANN Kerstin Bunte, Frank-Michael Schleif, Sven Haase, Thomas Villmann
2011	B	`conf`	Multivariate class labeling in Robust Soft LVQ. ESANN Petra Schneider, Tina Geweniger, Frank-Michael Schleif, Michael Biehl, Thomas Villmann
2011	B	`conf`	Prototype-Based Classification of Dissimilarity Data. IDA Barbara Hammer, Bassam Mokbel, Frank-Michael Schleif, Xibin Zhu
2011	B	`conf`	Recent trends in computational intelligence in life sciences. ESANN Udo Seiffert, Frank-Michael Schleif, Dietlind Zühlke
2011	—	`conf`	Relational Extensions of Learning Vector Quantization. ICONIP (2) Barbara Hammer, Frank-Michael Schleif, Xibin Zhu
2011	B	`conf`	Sparse kernelized vector quantization with local dependencies. IJCNN Frank-Michael Schleif
2011	J	`jnl`	Supervised learning of short and high-dimensional temporal sequences for life science measurements CoRR Frank-Michael Schleif, Andrej Gisbrecht, Barbara Hammer
2011	—	`conf`	Topographic Mapping of Dissimilarity Data. WSOM Barbara Hammer, Andrej Gisbrecht, Alexander Hasenfuss, Bassam Mokbel, Frank-Michael Schleif, Xibin Zhu
2010	J	`jnl`	Advances in computational intelligence and learning (ESANN 2009). Neurocomputing Cecilio Angulo, John Aldo Lee, Frank-Michael Schleif
2010	—	`conf`	Divergence Based Online Learning in Vector Quantization. ICAISC (1) Thomas Villmann, Sven Haase, Frank-Michael Schleif, Barbara Hammer
2010	B	`conf`	Divergence based Learning Vector Quantization. ESANN Ernest Mwebaze, Petra Schneider, Frank-Michael Schleif, Sven Haase, Thomas Villmann, Michael Biehl
2010	J	`jnl`	Evolving trees for the retrieval of mass spectrometry-based bacteria fingerprints. Knowl. Inf. Syst. Stephan Simmuteit, Frank-Michael Schleif, Thomas Villmann, Barbara Hammer
2010	C	`conf`	Generalized Derivative Based Kernelized Learning Vector Quantization. IDEAL Frank-Michael Schleif, Thomas Villmann, Barbara Hammer, Petra Schneider, Michael Biehl
2010	B	`conf`	Learning vector quantization for heterogeneous structured data. ESANN Dietlind Zühlke, Frank-Michael Schleif, Tina Geweniger, Sven Haase, Thomas Villmann
2010	B	`conf`	Sparse representation of data. ESANN Thomas Villmann, Frank-Michael Schleif, Barbara Hammer
2010	—	`conf`	The Mathematics of Divergence Based Online Learning in Vector Quantization. ANNPR Thomas Villmann, Sven Haase, Frank-Michael Schleif, Barbara Hammer, Michael Biehl
2009	J	`jnl`	Advances in machine learning and computational intelligence. Neurocomputing Frank-Michael Schleif, Michael Biehl, Alfredo Vellido
2009	J	`jnl`	Cancer informatics by prototype networks in mass spectrometry. Artif. Intell. Medicine Frank-Michael Schleif, Thomas Villmann, Markus Kostrzewa, Barbara Hammer, Alexander Gammerman
2009	—	`conf`	Funtional vector quantization by neural maps. WHISPERS Thomas Villmann, Frank-Michael Schleif
2009	—	`conf`	Hierarchical PCA Using Tree-SOM for the Identification of Bacteria. WSOM Stephan Simmuteit, Frank-Michael Schleif, Thomas Villmann, Markus Kostrzewa
2009	—	`conf`	Matrix Metric Adaptation Linear Discriminant Analysis of Biomedical Data. IWANN (1) Marc Strickert, Jens Keilwagen, Frank-Michael Schleif, Thomas Villmann, Michael Biehl
2009	B	`conf`	Neural Maps and Learning Vector Quantization - Theory and Applications. ESANN Frank-Michael Schleif, Thomas Villmann
2009	—	`ch.`	Prototype Based Classification in Bioinformatics. Encyclopedia of Artificial Intelligence Frank-Michael Schleif, Thomas Villmann, Barbara Hammer
2009	J	`jnl`	Supervised data analysis and reliability estimation with exemplary application for spectral data. Neurocomputing Frank-Michael Schleif, Thomas Villmann, Matthias Ongyerth
2009	C	`conf`	Tanimoto Metric in Tree-SOM for Improved Representation of Mass Spectrometry Data with an Underlying Taxonomic Structure. ICMLA Stephan Simmuteit, Frank-Michael Schleif, Thomas Villmann, Thomas Elssner
2009	—	`conf`	Unleashing Pearson Correlation for Faithful Analysis of Biomedical Data. Similarity-Based Clustering Marc Strickert, Frank-Michael Schleif, Thomas Villmann, Udo Seiffert
2008	—	`ch.`	Analysis of Spectral Data in Clinical Proteomics by Use of Learning Vector Quantizers. Computational Intelligence in Biomedicine and Bioinformatics Frank-Michael Schleif, Thomas Villmann, Barbara Hammer, Martijn van der Werff, André M. Deelder, Rob A. E. M. Tollenaar
2008	J	`jnl`	Classification of mass-spectrometric data in clinical proteomics using learning vector quantization methods. Briefings Bioinform. Thomas Villmann, Frank-Michael Schleif, Markus Kostrzewa, Axel Walch, Barbara Hammer
2008	—	`conf`	Comparison of Cluster Algorithms for the Analysis of Text Data Using Kolmogorov Complexity. ICONIP (2) Tina Geweniger, Frank-Michael Schleif, Alexander Hasenfuss, Barbara Hammer, Thomas Villmann
2008	J	`jnl`	Derivatives of Pearson Correlation for Gradient-based Analysis of Biomedical Data. Inteligencia Artif. Marc Strickert, Frank-Michael Schleif, Udo Seiffert, Thomas Villmann
2008	J	`jnl`	Fuzzy classification using information theoretic learning vector quantization. Neurocomputing Thomas Villmann, Barbara Hammer, Frank-Michael Schleif, Wieland Hermann, Marie Cottrell
2008	B	`conf`	Generalized matrix learning vector quantizer for the analysis of spectral data. ESANN Petra Schneider, Frank-Michael Schleif, Thomas Villmann, Michael Biehl
2008	B	`conf`	Metric adaptation for supervised attribute rating. ESANN Marc Strickert, Frank-Michael Schleif, Thomas Villmann
2008	J	`jnl`	Prototype based fuzzy classification in clinical proteomics. Int. J. Approx. Reason. Frank-Michael Schleif, Thomas Villmann, Barbara Hammer
2008	B	`conf`	Sparse Coding Neural Gas for Analysis of Nuclear Magnetic Resonance Spectroscopy. CBMS Frank-Michael Schleif, Matthias Ongyerth, Thomas Villmann
2007	—	`conf`	Advances in pre-processing and model generation for mass spectrometric data analysis. Similarity-based Clustering and its Application to Medicine and Biology Frank-Michael Schleif
2007	—	`conf`	Analysis of Proteomic Spectral Data by Multi Resolution Analysis and Self-Organizing Maps. WILF Frank-Michael Schleif, Thomas Villmann, Barbara Hammer
2007	C	`conf`	Association Learning in SOMs for Fuzzy-Classification. ICMLA Thomas Villmann, Frank-Michael Schleif, Martijn van der Werff, André M. Deelder, Rob A. E. M. Tollenaar
2007	Misc	`conf`	Fuzzy Labeled Self-Organizing Map for Classification of Spectra. IWANN Thomas Villmann, Frank-Michael Schleif, Erzsébet Merényi, Barbara Hammer
2007	B	`conf`	Intuitive Clustering of Biological Data. IJCNN Barbara Hammer, Alexander Hasenfuss, Frank-Michael Schleif, Thomas Villmann, Marc Strickert, Udo Seiffert
2007	J	`jnl`	Margin-based active learning for LVQ networks. Neurocomputing Frank-Michael Schleif, Barbara Hammer, Thomas Villmann
2007	J	`jnl`	Maschinelles Lernen mit Prototypmethoden in der klinischen Proteomik. Künstliche Intell. Frank-Michael Schleif
2007	Misc	`conf`	Neural Gas Clustering for Dissimilarity Data with Continuous Prototypes. IWANN Alexander Hasenfuss, Barbara Hammer, Frank-Michael Schleif, Thomas Villmann
2007	B	`conf`	Statistical Classification and Visualization of MALDI-Imaging Data. CBMS Marc Gerhard, Soren-Oliver Deininger, Frank-Michael Schleif
2007	Misc	`conf`	Supervised Neural Gas for Classification of Functional Data and Its Application to the Analysis of Clinical Proteom Spectra. IWANN Frank-Michael Schleif, Thomas Villmann, Barbara Hammer
2007	B	`conf`	Visualization of Fuzzy Information in Fuzzy-Classification for Image Segmentation using MDS. ESANN Thomas Villmann, Marc Strickert, Cornelia Brüß, Frank-Michael Schleif, Udo Seiffert
2006	B	`conf`	Analysis and Visualization of Proteomic Data by Fuzzy Labeled Self-Organizing Maps. CBMS Frank-Michael Schleif, Thomas Elssner, Markus Kostrzewa, Thomas Villmann, Barbara Hammer
2006	J	`jnl`	Comparison of relevance learning vector quantization with other metric adaptive classification methods. Neural Networks Thomas Villmann, Frank-Michael Schleif, Barbara Hammer
2006	—	`conf`	Fuzzy Labeled Self-Organizing Map with Label-Adjusted Prototypes. ANNPR Thomas Villmann, Udo Seiffert, Frank-Michael Schleif, Cornelia Brüß, Tina Geweniger, Barbara Hammer
2006	J	`jnl`	Fuzzy classification by fuzzy labeled neural gas. Neural Networks Thomas Villmann, Barbara Hammer, Frank-Michael Schleif, Tina Geweniger, Wieland Hermann
2006	B	`conf`	Fuzzy image segmentation with Fuzzy Labelled Neural Gas. ESANN Cornelia Brüß, Felix Bollenbeck, Frank-Michael Schleif, Winfriede Weschke, Thomas Villmann, Udo Seiffert
2006	Misc	`conf`	Learning Vector Quantization Classification with Local Relevance Determination for Medical Data. ICAISC Barbara Hammer, Thomas Villmann, Frank-Michael Schleif, Cornelia Albani, Wieland Hermann
2006	B	`conf`	Margin based Active Learning for LVQ Networks. ESANN Frank-Michael Schleif, Barbara Hammer, Thomas Villmann
2006	—	`conf`	Prototype Based Classification Using Information Theoretic Learning. ICONIP (2) Thomas Villmann, Barbara Hammer, Frank-Michael Schleif, Tina Geweniger, Tom Fischer, Marie Cottrell
2006	—	`—`	Prototype based machine learning for clinical proteomics. Frank-Michael Schleif
2006	—	`ch.`	Prototype based machine learning for clinical proteomics. Ausgezeichnete Informatikdissertationen Frank-Michael Schleif
2006	J	`jnl`	Prototype-based fuzzy classification with local relevance for proteomics. Neurocomputing Thomas Villmann, Frank-Michael Schleif, Barbara Hammer
2006	—	`conf`	Supervised Batch Neural Gas. ANNPR Barbara Hammer, Alexander Hasenfuss, Frank-Michael Schleif, Thomas Villmann
2005	C	`conf`	Fuzzy Labeled Soft Nearest Neighbor Classification with Relevance Learning. ICMLA Thomas Villmann, Frank-Michael Schleif, Barbara Hammer
2005	—	`conf`	Local Metric Adaptation for Soft Nearest Prototype Classification to Classify Proteomic Data. WILF Frank-Michael Schleif, Thomas Villmann, Barbara Hammer
2004	C	`conf`	Supervised relevance neural gas and unified maximum separability analysis for classification of mass spectrometric data. ICMLA Frank-Michael Schleif, U. Clauss, Thomas Villmann, Barbara Hammer