Prof. Dr. habil. Frank-Michael Schleif

Prof. Dr. habil.

Frank-Michael

Schleif

0931 351 18127

FIW (Informatik und Wirtschaftsinformatik)

Array

BIX.lab - Labor für Business Information

Datenbankmanagement und Business Intelligence

Datenanalyse, Maschinelles Lernen

Bruker Daltonik GmbH, Datenanalyse

Natural Sciences and Engineering Research Council of Canada, 2016
The Netherlands Genomics Initiative, 2010

Datenanalyse

GI, IEEE-CIS, ENNS, GNNS, DHV

Frank-Michael Schleif: Prototype based machine learning for clinical proteomics. Clausthal University of Technology, Clausthal-Zellerfeld, Lower Saxony, Germany 2006, pp. 1-133

eine leidlich vollständige Liste findet sich hier
http://dblp.uni-trier.de/pers/hd/s/Schleif:Frank=Michael

Christoph Raab, Frank-Michael Schleif: Transfer learning for the probabilistic classification vector machine. COPA 2018: 187-200

Mohammad Mohammadi, Reynier Peletier, Frank-Michael Schleif, Nicolai Petkov, Kerstin Bunte: Globular Cluster Detection in the Gaia Survey. ESANN 2018

Christoph Raab, Frank-Michael Schleif: Sparse Transfer Classification for Text Documents. KI 2018: 169-181

Frank-Michael Schleif, Christoph Raab, Peter Tiño: Sparsification of Indefinite Learning Models. S+SSPR 2018: 173-183

Frank-Michael Schleif: Indefinite Support Vector Regression. ICANN (2) 2017: 313-321

Frank-Michael Schleif: Small sets of random Fourier features by kernelized Matrix LVQ. WSOM 2017: 192-196

Frank-Michael Schleif, Ata Kabán, Peter Tiño: Finding Small Sets of Random Fourier Features for Shift-Invariant Kernel Approximation. ANNPR 2016: 42-54

Frank-Michael Schleif, Peter Tiño, Yingyu Liang: Learning in indefinite proximity spaces - recent trends. ESANN 2016

Kerstin Bunte, Marika Kaden, Frank-Michael Schleif: Low-Rank Kernel Space Representations in Prototype Learning. WSOM 2016: 341-353

Frank-Michael Schleif, Andrej Gisbrecht, Peter Tiño: Probabilistic classifiers with low rank indefinite kernels. CoRR abs/1604.02264 (2016)

Frank-Michael Schleif, Andrej Gisbrecht, Peter Tiño: Probabilistic Classification Vector Machine at large scale. ESANN 2015

Michael Biehl, Barbara Hammer, Frank-Michael Schleif, Petra Schneider, Thomas Villmann: Stationarity of Matrix Relevance LVQ. IJCNN 2015: 1-8

Frank-Michael Schleif, H. Chen, Peter Tiño: Incremental probabilistic classification vector machine with linear costs. IJCNN 2015: 1-8

Frank-Michael Schleif, Andrej Gisbrecht, Peter Tiño: Large Scale Indefinite Kernel Fisher Discriminant. SIMBAD 2015: 160-170

Frank-Michael Schleif: Proximity learning for non-standard big data. ESANN 2014

Frank-Michael Schleif, Peter Tiño, Thomas Villmann: Recent trends in learning of structured and non-standard data. ESANN 2014

Frank-Michael Schleif: Discriminative Fast Soft Competitive Learning. ICANN 2014: 81-88

Frank-Michael Schleif, Thomas Villmann, Xibin Zhu: High Dimensional Matrix Relevance Learning. ICDM Workshops 2014: 661-667

Tina Geweniger, Frank-Michael Schleif, Thomas Villmann: Probabilistic Prototype Classification Using t-norms. WSOM 2014: 99-108

Thomas Villmann, Frank-Michael Schleif, Marika Kaden, Mandy Lange: Advances in Self-Organizing Maps and Learning Vector Quantization - Proceedings of the 10th International Workshop, WSOM 2014,
Mittweida, Germany, July, 2-4, 2014. Advances in Intelligent Systems and Computing 295, Springer 2014, ISBN 978-3-319-07694-2 [contents]

Andrej Gisbrecht, Frank-Michael Schleif: Metric and non-metric proximity transformations at linear costs. CoRR abs/1411.1646 (2014)

Xibin Zhu, Frank-Michael Schleif, Barbara Hammer: Semi-Supervised Vector Quantization for proximity data. ESANN 2013

Frank-Michael Schleif, Xibin Zhu, Barbara Hammer: Sparse Prototype Representation by Core Sets. IDEAL 2013: 302-309

Xibin Zhu, Frank-Michael Schleif, Barbara Hammer: Secure Semi-supervised Vector Quantization for Dissimilarity Data. IWANN (1) 2013: 347-356

Frank-Michael Schleif, Andrej Gisbrecht: Data Analysis of (Non-)Metric Proximities at Linear Costs. SIMBAD 2013: 59-74

Frank-Michael Schleif, Xibin Zhu, Barbara Hammer: Soft Competitive Learning for Large Data Sets. ADBIS Workshops 2012: 141-151

Kerstin Bunte, Frank-Michael Schleif, Michael Biehl: Adaptive learning for complex-valued data. ESANN 2012

Barbara Hammer, Bassam Mokbel, Frank-Michael Schleif, Xibin Zhu: White Box Classification of Dissimilarity Data. HAIS (1) 2012: 309-321

Frank-Michael Schleif, Bassam Mokbel, Andrej Gisbrecht, Leslie Theunissen, Volker Dürr, Barbara Hammer:Learning Relevant Time Points for Time-Series Data in the Life Sciences. ICANN (2) 2012: 531-539

Frank-Michael Schleif, Xibin Zhu, Andrej Gisbrecht, Barbara Hammer: Fast approximated relational and kernel clustering. ICPR 2012: 1229-1232

Frank-Michael Schleif, Xibin Zhu, Barbara Hammer: A Conformal Classifier for Dissimilarity Data. AIAI (2) 2012: 234-243

Michael Biehl, Kerstin Bunte, Frank-Michael Schleif, Petra Schneider, Thomas Villmann: Large margin linear discriminative visualization by Matrix Relevance Learning. IJCNN 2012: 1-8

Frank-Michael Schleif, Andrej Gisbrecht, Barbara Hammer: Relevance learning for short high-dimensional time series in the life sciences. IJCNN 2012: 1-8

Xibin Zhu, Frank-Michael Schleif, Barbara Hammer: Patch Processing for Relational Learning Vector Quantization. ISNN (1) 2012: 55-63

Andrej Gisbrecht, Barbara Hammer, Frank-Michael Schleif, Xibin Zhu: Accelerating kernel clustering for biomedical data analysis. CIBCB 2011: 154-161

Kerstin Bunte, Frank-Michael Schleif, Sven Haase, Thomas Villmann: Mathematical Foundations of the Self Organized Neighbor Embedding (SONE) for Dimension Reduction and Visualization. ESANN 2011

Petra Schneider, Tina Geweniger, Frank-Michael Schleif, Michael Biehl, Thomas Villmann: Multivariate class labeling in Robust Soft LVQ. ESANN 2011

Udo Seiffert, Frank-Michael Schleif, Dietlind Zühlke: Recent trends in computational intelligence in life sciences. ESANN 2011

Frank-Michael Schleif, Andrej Gisbrecht, Barbara Hammer: Accelerating Kernel Neural Gas. ICANN (1) 2011: 150-158

Barbara Hammer, Frank-Michael Schleif, Xibin Zhu: Relational Extensions of Learning Vector Quantization. ICONIP (2) 2011: 481-489

Barbara Hammer, Bassam Mokbel, Frank-Michael Schleif, Xibin Zhu: Prototype-Based Classification of Dissimilarity Data. IDA 2011: 185-197

Andrej Gisbrecht, Frank-Michael Schleif, Xibin Zhu, Barbara Hammer: Linear Time Heuristics for Topographic Mapping of Dissimilarity Data. IDEAL 2011: 25-33

Frank-Michael Schleif: Sparse kernelized vector quantization with local dependencies. IJCNN 2011: 1538-1545

Barbara Hammer, Andrej Gisbrecht, Alexander Hasenfuss, Bassam Mokbel, Frank-Michael Schleif, Xibin Zhu: Topographic Mapping of Dissimilarity Data. WSOM 2011: 1-15

Frank-Michael Schleif, Andrej Gisbrecht, Barbara Hammer: Supervised learning of short and high-dimensional temporal sequences for life science measurements. CoRR abs/1110.2416 (2011)

Thomas Villmann, Sven Haase, Frank-Michael Schleif, Barbara Hammer, Michael Biehl: The Mathematics of Divergence Based Online Learning in Vector Quantization. ANNPR 2010: 108-119

Ernest Mwebaze, Petra Schneider, Frank-Michael Schleif, Sven Haase, Thomas Villmann, Michael Biehl:Divergence based Learning Vector Quantization. ESANN 2010
Thomas Villmann, Frank-Michael Schleif, Barbara Hammer: Sparse representation of data. ESANN 2010

Dietlind Zühlke, Frank-Michael Schleif, Tina Geweniger, Sven Haase, Thomas Villmann: Learning vector quantization for heterogeneous structured data. ESANN 2010

Thomas Villmann, Sven Haase, Frank-Michael Schleif, Barbara Hammer: Divergence Based Online Learning in Vector Quantization. ICAISC (1) 2010: 479-486

Frank-Michael Schleif, Thomas Villmann, Barbara Hammer, Petra Schneider, Michael Biehl: Generalized Derivative Based Kernelized Learning Vector Quantization. IDEAL 2010: 21-28

Marc Strickert, Frank-Michael Schleif, Thomas Villmann, Udo Seiffert: Unleashing Pearson Correlation for Faithful Analysis of Biomedical Data. Similarity-Based Clustering 2009: 70-91

Frank-Michael Schleif, Thomas Villmann: Neural Maps and Learning Vector Quantization - Theory and Applications. ESANN 2009

Stephan Simmuteit, Frank-Michael Schleif, Thomas Villmann, Thomas Elssner: Tanimoto Metric in Tree-SOM for Improved Representation of Mass Spectrometry Data with an Underlying Taxonomic Structure. ICMLA 2009: 563-567

Marc Strickert, Jens Keilwagen, Frank-Michael Schleif, Thomas Villmann, Michael Biehl:Matrix Metric Adaptation Linear Discriminant Analysis of Biomedical Data. IWANN (1) 2009: 933-940

Thomas Villmann, Frank-Michael Schleif: Funtional vector quantization by neural maps. WHISPERS 2009: 1-4

Stephan Simmuteit, Frank-Michael Schleif, Thomas Villmann, Markus Kostrzewa: Hierarchical PCA Using Tree-SOM for the Identification of Bacteria. WSOM 2009: 272-280

Frank-Michael Schleif, Thomas Villmann, Barbara Hammer: Prototype Based Classification in Bioinformatics. Encyclopedia of Artificial Intelligence 2009: 1337-1342

Frank-Michael Schleif, Matthias Ongyerth, Thomas Villmann: Sparse Coding Neural Gas for Analysis of Nuclear Magnetic Resonance Spectroscopy. CBMS 2008: 620-625

Marc Strickert, Frank-Michael Schleif, Thomas Villmann: Metric adaptation for supervised attribute rating. ESANN 2008: 31-36

Petra Schneider, Frank-Michael Schleif, Thomas Villmann, Michael Biehl: Generalized matrix learning vector quantizer for the analysis of spectral data. ESANN 2008: 451-456

Tina Geweniger, Frank-Michael Schleif, Alexander Hasenfuss, Barbara Hammer, Thomas Villmann: Comparison of Cluster Algorithms for the Analysis of Text Data Using Kolmogorov Complexity. ICONIP (2) 2008: 61-69

Frank-Michael Schleif, Thomas Villmann, Barbara Hammer, Martijn van der Werff, André M. Deelder, Rob A. E. M. Tollenaar: Analysis of Spectral Data in Clinical Proteomics by Use of Learning Vector Quantizers. Computational Intelligence in Biomedicine and Bioinformatics 2008: 141-167

Marc Gerhard, Soren-Oliver Deininger, Frank-Michael Schleif: Statistical Classification and Visualization of MALDI-Imaging Data. CBMS 2007: 403-405

Thomas Villmann, Marc Strickert, Cornelia Brüß, Frank-Michael Schleif, Udo Seiffert: Visualization of Fuzzy Information in Fuzzy-Classification for Image Segmentation using MDS. ESANN 2007: 103-108

Thomas Villmann, Frank-Michael Schleif, Martijn van der Werff, André M. Deelder, Rob A. E. M. Tollenaar: Association Learning in SOMs for Fuzzy-Classification. ICMLA 2007: 581-586

Barbara Hammer, Alexander Hasenfuss, Frank-Michael Schleif, Thomas Villmann, Marc Strickert, Udo Seiffert: Intuitive Clustering of Biological Data. IJCNN 2007: 1877-1882

Alexander Hasenfuss, Barbara Hammer, Frank-Michael Schleif, Thomas Villmann: Neural Gas Clustering for Dissimilarity Data with Continuous Prototypes. IWANN 2007: 539-546

Frank-Michael Schleif, Thomas Villmann, Barbara Hammer: Supervised Neural Gas for Classification of Functional Data and Its Application to the Analysis of Clinical Proteom Spectra. IWANN 2007: 1036-1044

Frank-Michael Schleif, Thomas Villmann, Barbara Hammer: Analysis of Proteomic Spectral Data by Multi Resolution Analysis and Self-Organizing Maps. WILF 2007: 563-570

Frank-Michael Schleif: Advances in pre-processing and model generation for mass spectrometric data analysis. Similarity-based Clustering and its Application to Medicine and Biology 2007

Barbara Hammer, Alexander Hasenfuss, Frank-Michael Schleif, Thomas Villmann: Supervised Batch Neural Gas. ANNPR 2006: 33-45

Thomas Villmann, Udo Seiffert, Frank-Michael Schleif, Cornelia Brüß, Tina Geweniger, Barbara Hammer: Fuzzy Labeled Self-Organizing Map with Label-Adjusted Prototypes. ANNPR 2006: 46-56

Frank-Michael Schleif, Thomas Elssner, Markus Kostrzewa, Thomas Villmann, Barbara Hammer: Analysis and Visualization of Proteomic Data by Fuzzy Labeled Self-Organizing Maps. CBMS 2006: 919-924

Frank-Michael Schleif, Barbara Hammer, Thomas Villmann: Margin based Active Learning for LVQ Networks. ESANN 2006: 539-544

Cornelia Brüß, Felix Bollenbeck, Frank-Michael Schleif, Winfriede Weschke, Thomas Villmann, Udo Seiffert: Fuzzy image segmentation with Fuzzy Labelled Neural Gas. ESANN 2006: 563-568

Frank-Michael Schleif: Prototype based machine learning for clinical proteomics. Ausgezeichnete Informatikdissertationen 2006: 179-188

Barbara Hammer, Thomas Villmann, Frank-Michael Schleif, Cornelia Albani, Wieland Hermann: Learning Vector Quantization Classification with Local Relevance Determination for Medical Data. ICAISC 2006: 603-612

Thomas Villmann, Barbara Hammer, Frank-Michael Schleif, Tina Geweniger, Tom Fischer, Marie Cottrell: Prototype Based Classification Using Information Theoretic Learning. ICONIP (2) 2006: 40-49
Thomas Villmann, Frank-Michael Schleif, Barbara Hammer: Fuzzy Labeled Soft Nearest Neighbor Classification with Relevance Learning. ICMLA 2005

Frank-Michael Schleif, Thomas Villmann, Barbara Hammer: Local Metric Adaptation for Soft Nearest Prototype Classification to Classify Proteomic Data. WILF 2005: 290-296

Frank-Michael Schleif, U. Clauss, Thomas Villmann, Barbara Hammer: Supervised relevance neural gas and unified maximum separability analysis for classification of mass spectrometric data. ICMLA 2004: 374-379

Frank-Michael Schleif, Andrej Gisbrecht, Peter Tiño: Supervised low rank indefinite kernel approximation using minimum enclosing balls. Neurocomputing 318: 213-226 (2018)

Frank-Michael Schleif, Andrej Gisbrecht, Peter Tiño: Large Scale Indefinite Kernel Fisher Discriminant. SIMBAD 2015: 160-170

Frank-Michael Schleif, Barbara Hammer, Javier Gonzalez Monroy, Javier González Jiménez, José-Luis Blanco-Claraco, Michael Biehl, Nicolai Petkov: Odor recognition in robotics applications by discriminative time-series modeling. Pattern Anal. Appl. 19(1): 207-220 (2016)

Frank-Michael Schleif, Xibin Zhu, Barbara Hammer: Sparse conformal prediction for dissimilarity data. Ann. Math. Artif. Intell. 74(1-2): 95-116 (2015)

Frank-Michael Schleif: Generic probabilistic prototype based classification of vectorial and proximity data. Neurocomputing 154: 208-216 (2015)

Andrej Gisbrecht, Frank-Michael Schleif: Metric and non-metric proximity transformations at linear costs. Neurocomputing 167: 643-657 (2015)

Michael Biehl, Alessandro Ghio, Frank-Michael Schleif: Developments in computational intelligence and machine learning. Neurocomputing 169: 185-186 (2015)

Bassam Mokbel, Benjamin Paaßen, Frank-Michael Schleif, Barbara Hammer: Metric learning for sequences in relational LVQ. Neurocomputing 169: 306-322 (2015)

Frank-Michael Schleif, Peter Tiño: Indefinite Proximity Learning: A Review. Neural Computation 27(10): 2039-2096 (2015)

Barbara Hammer, Daniela Hofmann, Frank-Michael Schleif, Xibin Zhu: Learning vector quantization for (dis-)similarities. Neurocomputing 131: 43-51 (2014)

Mark J. Embrechts, Fabrice Rossi, Frank-Michael Schleif, John Aldo Lee: Advances in artificial neural networks, machine learning, and computational intelligence (ESANN 2013). Neurocomputing 141: 1-2 (2014)

Daniela Hofmann, Frank-Michael Schleif, Benjamin Paaßen, Barbara Hammer: Learning interpretable kernelized prototype-based models. Neurocomputing 141: 84-96 (2014)

Marc Strickert, Kerstin Bunte, Frank-Michael Schleif, Eyke Hüllermeier: Correlation-based embedding of pairwise score data. Neurocomputing 141: 97-109 (2014)

Xibin Zhu, Frank-Michael Schleif, Barbara Hammer: Adaptive conformal semi-supervised vector quantization for dissimilarity data. Pattern Recognition Letters 49: 138-145 (2014)

Alessio Micheli, Frank-Michael Schleif, Peter Tiño: Novel approaches in machine learning and computational intelligence. Neurocomputing 112: 1-3 (2013)

Andrej Gisbrecht, Bassam Mokbel, Frank-Michael Schleif, Xibin Zhu, Barbara Hammer:
Linear Time Relational Prototype Based Learning. Int. J. Neural Syst. 22(5) (2012)

Xibin Zhu, Andrej Gisbrecht, Frank-Michael Schleif, Barbara Hammer: Approximation techniques for clustering dissimilarity data. Neurocomputing 90: 72-84 (2012)

Kerstin Bunte, Petra Schneider, Barbara Hammer, Frank-Michael Schleif, Thomas Villmann, Michael Biehl: Limited Rank Matrix Learning, discriminative dimension reduction and visualization. Neural Networks 26: 159-173 (2012)

Frank-Michael Schleif, T. Riemer, U. Börner, L. Schnapka-Hille, M. Cross: Genetic algorithm for shift-uncertainty correction in 1-D NMR-based metabolite identifications and quantifications. Bioinformatics 27(4): 524-533 (2011)

Frank-Michael Schleif, Thomas Villmann, Barbara Hammer, Petra Schneider: Efficient Kernelized Prototype Based Classification. Int. J. Neural Syst. 21(6): 443-457 (2011)

John Aldo Lee, Frank-Michael Schleif, Thomas Martinetz: Advances in artificial neural networks, machine learning, and computational intelligence. Neurocomputing 74(9): 1299-1300 (2011)

Ernest Mwebaze, Petra Schneider, Frank-Michael Schleif, Jennifer R. Aduwo, John A. Quinn, Sven Haase, Thomas Villmann, Michael Biehl: Divergence-based classification in learning vector quantization. Neurocomputing 74(9): 1429-1435 (2011)

Cecilio Angulo, John Aldo Lee, Frank-Michael Schleif: Advances in computational intelligence and learning (ESANN 2009). Neurocomputing 73(7-9): 1049-1050 (2010)

Stephan Simmuteit, Frank-Michael Schleif, Thomas Villmann, Barbara Hammer: Evolving trees for the retrieval of mass spectrometry-based bacteria fingerprints. Knowl. Inf. Syst. 25(2): 327-343 (2010)

Frank-Michael Schleif, Thomas Villmann, Markus Kostrzewa, Barbara Hammer, Alexander Gammerman: Cancer informatics by prototype networks in mass spectrometry. Artificial Intelligence in Medicine 45(2-3): 215-228 (2009)

Frank-Michael Schleif, Michael Biehl, Alfredo Vellido: Advances in machine learning and computational intelligence. Neurocomputing 72(7-9): 1377-1378 (2009)

Frank-Michael Schleif, Thomas Villmann, Matthias Ongyerth: Supervised data analysis and reliability estimation with exemplary application for spectral data. Neurocomputing 72(16-18): 3590-3601 (2009)

Marc Strickert, Frank-Michael Schleif, Udo Seiffert, Thomas Villmann: Derivatives of Pearson Correlation for Gradient-based Analysis of Biomedical Data. Inteligencia Artificial, Revista Iberoamericana de Inteligencia Artificial 12(37): 37-44 (2008)

Thomas Villmann, Frank-Michael Schleif, Markus Kostrzewa, Axel Walch, Barbara Hammer: Classification of mass-spectrometric data in clinical proteomics using learning vector quantization methods. Briefings in Bioinformatics 9(2): 129-143 (2008)

Frank-Michael Schleif, Thomas Villmann, Barbara Hammer: Prototype based fuzzy classification in clinical proteomics. Int. J. Approx. Reasoning 47(1): 4-16 (2008)

Thomas Villmann, Barbara Hammer, Frank-Michael Schleif, Wieland Hermann, Marie Cottrell: Fuzzy classification using information theoretic learning vector quantization. Neurocomputing 71(16-18): 3070-3076 (2008)

Frank-Michael Schleif, Barbara Hammer, Thomas Villmann: Margin-based active learning for LVQ networks. Neurocomputing 70(7-9): 1215-1224 (2007)

Frank-Michael Schleif: Maschinelles Lernen mit Prototypmethoden in der klinischen Proteomik. KI 21(4): 65-67 (2007)

Thomas Villmann, Frank-Michael Schleif, Barbara Hammer: Prototype-based fuzzy classification with local relevance for proteomics. Neurocomputing 69(16-18): 2425-2428 (2006)

Thomas Villmann, Frank-Michael Schleif, Barbara Hammer: Comparison of relevance learning vector quantization with other metric adaptive classification methods. Neural Networks 19(5): 610-622 (2006)

Thomas Villmann, Barbara Hammer, Frank-Michael Schleif, Tina Geweniger, Wieland Hermann: Fuzzy classification by fuzzy labeled neural gas. Neural Networks 19(6-7): 772-779 (2006)