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ORCIDFrank-Michael Schleif
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- affiliation: Bielefeld University, Germany
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2020 – today
- 2024
[c103]Manuel Röder, Frank-Michael Schleif:
Deep Transfer Hashing for Adaptive Learning on Federated Streaming Data. IAL@PKDD/ECML 2024: 7-11- [c102]Manuel Röder, Maximilian Münch, Christoph Raab, Frank-Michael Schleif:
Crossing Domain Borders with Federated Few-Shot Adaptation. ICPRAM 2024: 511-521 - [i13]Manuel Röder, Frank-Michael Schleif:
Sparse Uncertainty-Informed Sampling from Federated Streaming Data. CoRR abs/2408.17108 (2024) - [i12]Manuel Röder, Frank-Michael Schleif:
Deep Transfer Hashing for Adaptive Learning on Federated Streaming Data. CoRR abs/2409.12575 (2024) - 2023
- [j47]Maximilian Münch
, Manuel Röder, Simon Heilig, Christoph Raab, Frank-Michael Schleif:
Static and adaptive subspace information fusion for indefinite heterogeneous proximity data. Neurocomputing 555: 126635 (2023) - [c101]Maximilian Münch, Manuel Röder, Frank-Michael Schleif:
Unlocking the Potential of Non-PSD Kernel Matrices: A Polar Decomposition-based Transformation for Improved Prediction Models. CIKM 2023: 1867-1876 - [c100]Maximilian Münch, Katrin Sophie Bohnsack, Alexander Engelsberger, Frank-Michael Schleif, Thomas Villmann:
Sparse Nyström Approximation for Non-Vectorial Data Using Class-informed Landmark Selection. ESANN 2023 - [c99]Lukas Ewecker, Timo Winkler, Philipp Väth, Robin Schwager, Tim Brühl, Frank-Michael Schleif:
How Important is the Temporal Context to Anticipate Oncoming Vehicles at Night? SMC 2023: 1000-1007 - [i11]Manuel Röder, Leon Heller, Maximilian Münch, Frank-Michael Schleif:
Efficient Cross-Domain Federated Learning by MixStyle Approximation. CoRR abs/2312.07064 (2023) - 2022
- [j46]Moritz Heusinger, Christoph Raab, Frank-Michael Schleif:
Dimensionality reduction in the context of dynamic social media data streams. Evol. Syst. 13(3): 387-401 (2022) - [j45]Christoph Raab, Manuel Röder, Frank-Michael Schleif:
Domain adversarial tangent subspace alignment for explainable domain adaptation. Neurocomputing 506: 418-429 (2022) - [j44]Luca Oneto, Nicolò Navarin, Frank-Michael Schleif:
Advances in artificial neural networks, machine learning and computational intelligence. Neurocomputing 507: 311-314 (2022) - [j43]Moritz Heusinger, Christoph Raab, Frank-Michael Schleif:
Passive concept drift handling via variations of learning vector quantization. Neural Comput. Appl. 34(1): 89-100 (2022) - [c98]Maximilian Münch, Christoph Raab, Simon Heilig, Manuel Röder, Frank-Michael Schleif:
Adaptive multi-modal positive semi-definite and indefinite kernel fusion for binary classification. ESANN 2022 - [c97]Moritz Heusinger, Frank-Michael Schleif:
A Streaming Approach to the Core Vector Machine. ICAISC (2) 2022: 91-101 - [c96]Simon Heilig, Maximilian Münch, Frank-Michael Schleif:
Memory Efficient Kernel Approximation for Non-Stationary and Indefinite Kernels. IJCNN 2022: 1-8 - [i10]Moritz Heusinger, Christoph Raab, Fabrice Rossi, Frank-Michael Schleif:
Federated Learning - Methods, Applications and beyond. CoRR abs/2212.11729 (2022) - 2021
- [c95]Moritz Heusinger, Christoph Raab, Fabrice Rossi, Frank-Michael Schleif:
Federated Learning - Methods, Applications and beyond. ESANN 2021 - [c94]Maximilian Münch, Simon Heilig, Frank-Michael Schleif:
Multi-perspective embedding for non-metric time series classification. ESANN 2021 - [c93]Christoph Raab, Sascha Saralajew, Frank-Michael Schleif:
Domain Adversarial Tangent Learning Towards Interpretable Domain Adaptation. ESANN 2021 - [c92]Moritz Heusinger, Frank-Michael Schleif:
Classification in Non-stationary Environments Using Coresets over Sliding Windows. IWANN (1) 2021: 126-137 - [c91]Maximilian Münch, Simon Heilig, Philipp Väth, Frank-Michael Schleif:
Scalable embedding of multiple perspectives for indefinite life-science data analysis. SSCI 2021: 1-8 - [i9]Simon Heilig, Maximilian Münch, Frank-Michael Schleif:
Revisiting Memory Efficient Kernel Approximation: An Indefinite Learning Perspective. CoRR abs/2112.09893 (2021) - 2020
- [j42]Maximilian Münch, Christoph Raab, Michael Biehl, Frank-Michael Schleif:
Data-Driven Supervised Learning for Life Science Data. Frontiers Appl. Math. Stat. 6: 553000 (2020) - [j41]Christoph Raab, Frank-Michael Schleif:
Transfer learning extensions for the probabilistic classification vector machine. Neurocomputing 397: 320-330 (2020) - [j40]Christoph Raab, Moritz Heusinger, Frank-Michael Schleif:
Reactive Soft Prototype Computing for Concept Drift Streams. Neurocomputing 416: 340-351 (2020) - [j39]Frank-Michael Schleif, Christoph Raab, Peter Tiño:
Sparsification of core set models in non-metric supervised learning. Pattern Recognit. Lett. 129: 1-7 (2020) - [c90]Christoph Raab, Philipp Väth, Peter Meier, Frank-Michael Schleif:
Bridging Adversarial and Statistical Domain Transfer via Spectral Adaptation Networks. ACCV (3) 2020: 457-473 - [c89]Moritz Heusinger, Christoph Raab, Frank-Michael Schleif:
Analyzing Dynamic Social Media Data via Random Projection - A New Challenge for Stream Classifiers. EAIS 2020: 1-8 - [c88]Moritz Heusinger, Frank-Michael Schleif:
Random Projection in supervised non-stationary environments. ESANN 2020: 405-410 - [c87]Christoph Raab, Peter Meier, Frank-Michael Schleif:
Domain Invariant Representations with Deep Spectral Alignment. ESANN 2020: 509-514 - [c86]Moritz Heusinger, Frank-Michael Schleif:
Random Projection in the Presence of Concept Drift in Supervised Environments. ICAISC (1) 2020: 514-524 - [c85]Maximilian Münch, Christoph Raab, Michael Biehl, Frank-Michael Schleif:
Structure Preserving Encoding of Non-euclidean Similarity Data. ICPRAM 2020: 43-51 - [c84]Maximilian Münch, Christoph Raab, Frank-Michael Schleif:
Encoding of Indefinite Proximity Data: A Structure Preserving Perspective. ICPRAM (Revised Selected Papers) 2020: 112-137 - [c83]Christoph Raab, Frank-Michael Schleif:
Low-Rank Subspace Override for Unsupervised Domain Adaptation. KI 2020: 132-147 - [c82]Moritz Heusinger, Frank-Michael Schleif:
Reactive Concept Drift Detection Using Coresets Over Sliding Windows. SSCI 2020: 1350-1355 - [c81]Maximilian Münch, Michiel Straat, Michael Biehl, Frank-Michael Schleif:
Complex-Valued Embeddings of Generic Proximity Data. S+SSPR 2020: 14-23 - [i8]Christoph Raab, Moritz Heusinger, Frank-Michael Schleif:
Reactive Soft Prototype Computing for Concept Drift Streams. CoRR abs/2007.05432 (2020) - [i7]Christoph Raab, Frank-Michael Schleif:
Transfer learning extensions for the probabilistic classification vector machine. CoRR abs/2007.07090 (2020) - [i6]Maximilian Münch, Michiel Straat, Michael Biehl, Frank-Michael Schleif:
Complex-valued embeddings of generic proximity data. CoRR abs/2008.13454 (2020)
2010 – 2019
- 2019
- [j38]Luca Oneto, Kerstin Bunte, Frank-Michael Schleif:
Advances in artificial neural networks, machine learning and computational intelligence: Selected papers from the 26th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2018). Neurocomputing 342: 1-5 (2019) - [j37]Mohammad Mohammadi, Nicolai Petkov, Kerstin Bunte, Reynier F. Peletier, Frank-Michael Schleif:
Globular cluster detection in the GAIA survey. Neurocomputing 342: 164-171 (2019) - [c80]Albert Bifet, Barbara Hammer, Frank-Michael Schleif:
Recent trends in streaming data analysis, concept drift and analysis of dynamic data sets. ESANN 2019 - [c79]Maximilian Münch, Karsten Huffstadt, Frank-Michael Schleif:
Towards a device-free passive presence detection system with Bluetooth Low Energy beacons. ESANN 2019 - [c78]Christoph Raab, Moritz Heusinger, Frank-Michael Schleif:
Reactive Soft Prototype Computing for frequent reoccurring Concept Drift. ESANN 2019 - [c77]Maximilian Münch, Frank-Michael Schleif:
Device-Free Passive Human Counting with Bluetooth Low Energy Beacons. IWANN (2) 2019: 799-810 - [c76]Moritz Heusinger, Christoph Raab, Frank-Michael Schleif:
Passive Concept Drift Handling via Momentum Based Robust Soft Learning Vector Quantization. WSOM+ 2019: 200-209 - [i5]Christoph Raab, Frank-Michael Schleif:
Domain Adaptation via Low-Rank Basis Approximation. CoRR abs/1907.01343 (2019) - 2018
- [j36]Frank-Michael Schleif, Andrej Gisbrecht, Peter Tiño:
Supervised low rank indefinite kernel approximation using minimum enclosing balls. Neurocomputing 318: 213-226 (2018) - [c75]Christoph Raab, Frank-Michael Schleif:
Transfer learning for the probabilistic classification vector machine. COPA 2018: 187-200 - [c74]Mohammad Mohammadi, Reynier Peletier, Frank-Michael Schleif, Nicolai Petkov, Kerstin Bunte:
Globular Cluster Detection in the Gaia Survey. ESANN 2018 - [c73]Christoph Raab, Frank-Michael Schleif:
Sparse Transfer Classification for Text Documents. KI 2018: 169-181 - [c72]Frank-Michael Schleif, Christoph Raab, Peter Tiño:
Sparsification of Indefinite Learning Models. S+SSPR 2018: 173-183 - 2017
- [j35]Frank-Michael Schleif, Peter Tiño:
Indefinite Core Vector Machine. Pattern Recognit. 71: 187-195 (2017) - [c71]Frank-Michael Schleif:
Indefinite Support Vector Regression. ICANN (2) 2017: 313-321 - [c70]Frank-Michael Schleif:
Small sets of random Fourier features by kernelized Matrix LVQ. WSOM 2017: 192-196 - 2016
- [j34]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) - [c69]Frank-Michael Schleif, Ata Kabán, Peter Tiño:
Finding Small Sets of Random Fourier Features for Shift-Invariant Kernel Approximation. ANNPR 2016: 42-54 - [c68]Frank-Michael Schleif, Peter Tiño, Yingyu Liang:
Learning in indefinite proximity spaces - recent trends. ESANN 2016 - [c67]Kerstin Bunte, Marika Kaden, Frank-Michael Schleif:
Low-Rank Kernel Space Representations in Prototype Learning. WSOM 2016: 341-353 - [i4]Frank-Michael Schleif, Andrej Gisbrecht, Peter Tiño:
Probabilistic classifiers with low rank indefinite kernels. CoRR abs/1604.02264 (2016) - 2015
- [j33]Frank-Michael Schleif, Xibin Zhu, Barbara Hammer:
Sparse conformal prediction for dissimilarity data. Ann. Math. Artif. Intell. 74(1-2): 95-116 (2015) - [j32]Frank-Michael Schleif:
Generic probabilistic prototype based classification of vectorial and proximity data. Neurocomputing 154: 208-216 (2015) - [j31]Andrej Gisbrecht, Frank-Michael Schleif:
Metric and non-metric proximity transformations at linear costs. Neurocomputing 167: 643-657 (2015) - [j30]Michael Biehl, Alessandro Ghio, Frank-Michael Schleif:
Developments in computational intelligence and machine learning. Neurocomputing 169: 185-186 (2015) - [j29]Bassam Mokbel, Benjamin Paaßen, Frank-Michael Schleif, Barbara Hammer:
Metric learning for sequences in relational LVQ. Neurocomputing 169: 306-322 (2015) - [j28]Frank-Michael Schleif, Peter Tiño:
Indefinite Proximity Learning: A Review. Neural Comput. 27(10): 2039-2096 (2015) - [c66]Frank-Michael Schleif, Andrej Gisbrecht, Peter Tiño:
Probabilistic Classification Vector Machine at large scale. ESANN 2015 - [c65]Michael Biehl, Barbara Hammer, Frank-Michael Schleif, Petra Schneider, Thomas Villmann:
Stationarity of Matrix Relevance LVQ. IJCNN 2015: 1-8 - [c64]Frank-Michael Schleif, H. Chen, Peter Tiño:
Incremental probabilistic classification vector machine with linear costs. IJCNN 2015: 1-8 - [c63]Frank-Michael Schleif, Andrej Gisbrecht, Peter Tiño:
Large Scale Indefinite Kernel Fisher Discriminant. SIMBAD 2015: 160-170 - 2014
- [j27]Barbara Hammer, Daniela Hofmann, Frank-Michael Schleif, Xibin Zhu:
Learning vector quantization for (dis-)similarities. Neurocomputing 131: 43-51 (2014) - [j26]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) - [j25]Daniela Hofmann, Frank-Michael Schleif, Benjamin Paaßen, Barbara Hammer:
Learning interpretable kernelized prototype-based models. Neurocomputing 141: 84-96 (2014) - [j24]Marc Strickert, Kerstin Bunte, Frank-Michael Schleif, Eyke Hüllermeier:
Correlation-based embedding of pairwise score data. Neurocomputing 141: 97-109 (2014) - [j23]Xibin Zhu, Frank-Michael Schleif, Barbara Hammer:
Adaptive conformal semi-supervised vector quantization for dissimilarity data. Pattern Recognit. Lett. 49: 138-145 (2014) - [c62]Frank-Michael Schleif:
Proximity learning for non-standard big data. ESANN 2014 - [c61]Frank-Michael Schleif, Peter Tiño, Thomas Villmann:
Recent trends in learning of structured and non-standard data. ESANN 2014 - [c60]Frank-Michael Schleif:
Discriminative Fast Soft Competitive Learning. ICANN 2014: 81-88 - [c59]Frank-Michael Schleif, Thomas Villmann, Xibin Zhu:
High Dimensional Matrix Relevance Learning. ICDM Workshops 2014: 661-667 - [c58]Tina Geweniger, Frank-Michael Schleif, Thomas Villmann:
Probabilistic Prototype Classification Using t-norms. WSOM 2014: 99-108 - [e1]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] - [i3]Andrej Gisbrecht, Frank-Michael Schleif:
Metric and non-metric proximity transformations at linear costs. CoRR abs/1411.1646 (2014) - 2013
- [j22]Alessio Micheli, Frank-Michael Schleif, Peter Tiño:
Novel approaches in machine learning and computational intelligence. Neurocomputing 112: 1-3 (2013) - [c57]Xibin Zhu, Frank-Michael Schleif, Barbara Hammer:
Semi-Supervised Vector Quantization for proximity data. ESANN 2013 - [c56]Frank-Michael Schleif, Xibin Zhu, Barbara Hammer:
Sparse Prototype Representation by Core Sets. IDEAL 2013: 302-309 - [c55]Xibin Zhu, Frank-Michael Schleif, Barbara Hammer:
Secure Semi-supervised Vector Quantization for Dissimilarity Data. IWANN (1) 2013: 347-356 - [c54]Frank-Michael Schleif, Andrej Gisbrecht:
Data Analysis of (Non-)Metric Proximities at Linear Costs. SIMBAD 2013: 59-74 - 2012
- [j21]Andrej Gisbrecht, Bassam Mokbel, Frank-Michael Schleif, Xibin Zhu, Barbara Hammer:
Linear Time Relational Prototype Based Learning. Int. J. Neural Syst. 22(5) (2012) - [j20]Xibin Zhu, Andrej Gisbrecht, Frank-Michael Schleif, Barbara Hammer:
Approximation techniques for clustering dissimilarity data. Neurocomputing 90: 72-84 (2012) - [j19]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) - [c53]Frank-Michael Schleif, Xibin Zhu, Barbara Hammer:
Soft Competitive Learning for Large Data Sets. ADBIS Workshops 2012: 141-151 - [c52]Kerstin Bunte, Frank-Michael Schleif, Michael Biehl:
Adaptive learning for complex-valued data. ESANN 2012 - [c51]Barbara Hammer, Bassam Mokbel, Frank-Michael Schleif, Xibin Zhu:
White Box Classification of Dissimilarity Data. HAIS (1) 2012: 309-321 - [c50]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 - [c49]Frank-Michael Schleif, Xibin Zhu, Andrej Gisbrecht, Barbara Hammer:
Fast approximated relational and kernel clustering. ICPR 2012: 1229-1232 - [c48]Frank-Michael Schleif, Xibin Zhu, Barbara Hammer:
A Conformal Classifier for Dissimilarity Data. AIAI (2) 2012: 234-243 - [c47]Michael Biehl, Kerstin Bunte, Frank-Michael Schleif, Petra Schneider, Thomas Villmann:
Large margin linear discriminative visualization by Matrix Relevance Learning. IJCNN 2012: 1-8 - [c46]Frank-Michael Schleif, Andrej Gisbrecht, Barbara Hammer:
Relevance learning for short high-dimensional time series in the life sciences. IJCNN 2012: 1-8 - [c45]Xibin Zhu, Frank-Michael Schleif, Barbara Hammer:
Patch Processing for Relational Learning Vector Quantization. ISNN (1) 2012: 55-63 - 2011
- [j18]Frank-Michael Schleif, T. Riemer, U. Börner, L. Schnapka-Hille, Michael Cross:
Genetic algorithm for shift-uncertainty correction in 1-D NMR-based metabolite identifications and quantifications. Bioinform. 27(4): 524-533 (2011) - [j17]Frank-Michael Schleif, Thomas Villmann, Barbara Hammer, Petra Schneider:
Efficient Kernelized Prototype Based Classification. Int. J. Neural Syst. 21(6): 443-457 (2011) - [j16]John Aldo Lee, Frank-Michael Schleif, Thomas Martinetz:
Advances in artificial neural networks, machine learning, and computational intelligence. Neurocomputing 74(9): 1299-1300 (2011) - [j15]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) - [c44]Andrej Gisbrecht, Barbara Hammer, Frank-Michael Schleif, Xibin Zhu:
Accelerating kernel clustering for biomedical data analysis. CIBCB 2011: 154-161 - [c43]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 - [c42]Petra Schneider, Tina Geweniger, Frank-Michael Schleif, Michael Biehl, Thomas Villmann:
Multivariate class labeling in Robust Soft LVQ. ESANN 2011 - [c41]Udo Seiffert, Frank-Michael Schleif, Dietlind Zühlke:
Recent trends in computational intelligence in life sciences. ESANN 2011 - [c40]Frank-Michael Schleif, Andrej Gisbrecht, Barbara Hammer:
Accelerating Kernel Neural Gas. ICANN (1) 2011: 150-158 - [c39]Barbara Hammer, Frank-Michael Schleif, Xibin Zhu:
Relational Extensions of Learning Vector Quantization. ICONIP (2) 2011: 481-489 - [c38]Barbara Hammer, Bassam Mokbel, Frank-Michael Schleif, Xibin Zhu:
Prototype-Based Classification of Dissimilarity Data. IDA 2011: 185-197 - [c37]Andrej Gisbrecht, Frank-Michael Schleif, Xibin Zhu, Barbara Hammer:
Linear Time Heuristics for Topographic Mapping of Dissimilarity Data. IDEAL 2011: 25-33 - [c36]Frank-Michael Schleif:
Sparse kernelized vector quantization with local dependencies. IJCNN 2011: 1538-1545 - [c35]Barbara Hammer, Andrej Gisbrecht, Alexander Hasenfuss, Bassam Mokbel, Frank-Michael Schleif, Xibin Zhu:
Topographic Mapping of Dissimilarity Data. WSOM 2011: 1-15 - [i2]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) - 2010
- [j14]Cecilio Angulo, John Aldo Lee, Frank-Michael Schleif:
Advances in computational intelligence and learning (ESANN 2009). Neurocomputing 73(7-9): 1049-1050 (2010) - [j13]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) - [c34]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 - [c33]Ernest Mwebaze, Petra Schneider, Frank-Michael Schleif, Sven Haase, Thomas Villmann, Michael Biehl:
Divergence based Learning Vector Quantization. ESANN 2010 - [c32]Thomas Villmann, Frank-Michael Schleif, Barbara Hammer:
Sparse representation of data. ESANN 2010 - [c31]Dietlind Zühlke, Frank-Michael Schleif, Tina Geweniger, Sven Haase, Thomas Villmann:
Learning vector quantization for heterogeneous structured data. ESANN 2010 - [c30]Thomas Villmann, Sven Haase, Frank-Michael Schleif, Barbara Hammer:
Divergence Based Online Learning in Vector Quantization. ICAISC (1) 2010: 479-486 - [c29]Frank-Michael Schleif, Thomas Villmann, Barbara Hammer, Petra Schneider, Michael Biehl:
Generalized Derivative Based Kernelized Learning Vector Quantization. IDEAL 2010: 21-28
2000 – 2009
- 2009
- [j12]Frank-Michael Schleif, Thomas Villmann, Markus Kostrzewa, Barbara Hammer, Alexander Gammerman:
Cancer informatics by prototype networks in mass spectrometry. Artif. Intell. Medicine 45(2-3): 215-228 (2009) - [j11]Frank-Michael Schleif, Michael Biehl, Alfredo Vellido:
Advances in machine learning and computational intelligence. Neurocomputing 72(7-9): 1377-1378 (2009) - [j10]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) - [c28]Marc Strickert, Frank-Michael Schleif, Thomas Villmann, Udo Seiffert:
Unleashing Pearson Correlation for Faithful Analysis of Biomedical Data. Similarity-Based Clustering 2009: 70-91 - [c27]Frank-Michael Schleif, Thomas Villmann:
Neural Maps and Learning Vector Quantization - Theory and Applications. ESANN 2009 - [c26]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 - [c25]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 - [c24]Thomas Villmann, Frank-Michael Schleif:
Funtional vector quantization by neural maps. WHISPERS 2009: 1-4 - [c23]Stephan Simmuteit, Frank-Michael Schleif, Thomas Villmann, Markus Kostrzewa:
Hierarchical PCA Using Tree-SOM for the Identification of Bacteria. WSOM 2009: 272-280 - [r1]Frank-Michael Schleif, Thomas Villmann, Barbara Hammer:
Prototype Based Classification in Bioinformatics. Encyclopedia of Artificial Intelligence 2009: 1337-1342 - 2008
- [j9]Marc Strickert, Frank-Michael Schleif, Udo Seiffert, Thomas Villmann:
Derivatives of Pearson Correlation for Gradient-based Analysis of Biomedical Data. Inteligencia Artif. 12(37): 37-44 (2008) - [j8]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 Bioinform. 9(2): 129-143 (2008) - [j7]Frank-Michael Schleif, Thomas Villmann, Barbara Hammer:
Prototype based fuzzy classification in clinical proteomics. Int. J. Approx. Reason. 47(1): 4-16 (2008) - [j6]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) - [c22]Frank-Michael Schleif, Matthias Ongyerth, Thomas Villmann:
Sparse Coding Neural Gas for Analysis of Nuclear Magnetic Resonance Spectroscopy. CBMS 2008: 620-625 - [c21]Marc Strickert, Frank-Michael Schleif, Thomas Villmann:
Metric adaptation for supervised attribute rating. ESANN 2008: 31-36 - [c20]Petra Schneider, Frank-Michael Schleif, Thomas Villmann, Michael Biehl:
Generalized matrix learning vector quantizer for the analysis of spectral data. ESANN 2008: 451-456 - [c19]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 - [p2]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 - 2007
- [j5]Frank-Michael Schleif, Barbara Hammer, Thomas Villmann:
Margin-based active learning for LVQ networks. Neurocomputing 70(7-9): 1215-1224 (2007) - [j4]Frank-Michael Schleif:
Maschinelles Lernen mit Prototypmethoden in der klinischen Proteomik. Künstliche Intell. 21(4): 65-67 (2007) - [c18]Marc Gerhard, Soren-Oliver Deininger, Frank-Michael Schleif:
Statistical Classification and Visualization of MALDI-Imaging Data. CBMS 2007: 403-405 - [c17]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 - [c16]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 - [c15]Barbara Hammer, Alexander Hasenfuss, Frank-Michael Schleif, Thomas Villmann, Marc Strickert, Udo Seiffert:
Intuitive Clustering of Biological Data. IJCNN 2007: 1877-1882 - [c14]Alexander Hasenfuss, Barbara Hammer, Frank-Michael Schleif, Thomas Villmann:
Neural Gas Clustering for Dissimilarity Data with Continuous Prototypes. IWANN 2007: 539-546 - [c13]Thomas Villmann, Frank-Michael Schleif, Erzsébet Merényi, Barbara Hammer:
Fuzzy Labeled Self-Organizing Map for Classification of Spectra. IWANN 2007: 556-563 - [c12]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 - [c11]Frank-Michael Schleif, Thomas Villmann, Barbara Hammer:
Analysis of Proteomic Spectral Data by Multi Resolution Analysis and Self-Organizing Maps. WILF 2007: 563-570 - [i1]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 - 2006
- [b1]Frank-Michael Schleif:
Prototype based machine learning for clinical proteomics. Clausthal University of Technology, Clausthal-Zellerfeld, Lower Saxony, Germany, 2006, pp. 1-133 - [j3]Thomas Villmann, Frank-Michael Schleif, Barbara Hammer:
Prototype-based fuzzy classification with local relevance for proteomics. Neurocomputing 69(16-18): 2425-2428 (2006) - [j2]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) - [j1]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) - [c10]Barbara Hammer, Alexander Hasenfuss, Frank-Michael Schleif, Thomas Villmann:
Supervised Batch Neural Gas. ANNPR 2006: 33-45 - [c9]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 - [c8]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 - [c7]Frank-Michael Schleif, Barbara Hammer, Thomas Villmann:
Margin based Active Learning for LVQ Networks. ESANN 2006: 539-544 - [c6]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 - [c5]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 - [c4]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 - [p1]Frank-Michael Schleif:
Prototype based machine learning for clinical proteomics. Ausgezeichnete Informatikdissertationen 2006: 179-188 - 2005
- [c3]Thomas Villmann, Frank-Michael Schleif, Barbara Hammer:
Fuzzy Labeled Soft Nearest Neighbor Classification with Relevance Learning. ICMLA 2005 - [c2]Frank-Michael Schleif, Thomas Villmann, Barbara Hammer:
Local Metric Adaptation for Soft Nearest Prototype Classification to Classify Proteomic Data. WILF 2005: 290-296 - 2004
- [c1]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
Coauthor Index
[c80] [j34] [j33] [j29] [c65] [j27] [j25] [j23] [c57] [c56] [c55] [j21] [j20] [j19] [c53] [c51] [c50] [c49] [c48] [c46] [c45] [j17] [c44] [c40] [c39] [c38] [c37] [c35] [i2] [j13] [c34] [c32] [c30] [c29] [j12] [r1] [j8] [j7] [j6] [c19] [p2] [j5] [c15] [c14] [c13] [c12] [c11] [j3] [j2] [j1] [c10] [c9] [c8] [c7] [c5] [c4] [c3] [c2] [c1]
[c100] [c65] [c61] [c59] [c58] [e1] [j19] [c47] [j17] [j15] [c43] [c42] [j13] [c34] [c33] [c32] [c31] [c30] [c29] [j12] [j10] [c28] [c27] [c26] [c25] [c24] [c23] [r1] [j9] [j8] [j7] [j6] [c22] [c21] [c20] [c19] [p2] [j5] [c17] [c16] [c15] [c14] [c13] [c12] [c11] [j3] [j2] [j1] [c10] [c9] [c8] [c7] [c6] [c5] [c4] [c3] [c2] [c1]
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