Frank-Michael Schleif

ORCID

Publication Activity (10 Years)

Years Active: 2004-2024
Publications (10 Years): 58

Top Venues

Clausthal University of Technology, Clausthal-Zellerfeld, Lower Saxony, Germany (PHD)

Education History

Clausthal University of Technology, Clausthal-Zellerfeld, Lower Saxony, Germany

PHD

Publications

Manuel Röder, Maximilian Münch, Christoph Raab, Frank-Michael Schleif
Crossing Domain Borders with Federated Few-Shot Adaptation. ICPRAM (2024)
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)
Maximilian Münch, Manuel Röder, Simon Heilig, Christoph Raab, Frank-Michael Schleif
Static and adaptive subspace information fusion for indefinite heterogeneous proximity data. Neurocomputing 555 (2023)
Manuel Röder, Leon Heller, Maximilian Münch, Frank-Michael Schleif
Efficient Cross-Domain Federated Learning by MixStyle Approximation. CoRR (2023)
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)
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)
Moritz Heusinger, Frank-Michael Schleif
A Streaming Approach to the Core Vector Machine. ICAISC (2) (2022)
Luca Oneto, Nicolò Navarin, Frank-Michael Schleif
Advances in artificial neural networks, machine learning and computational intelligence. Neurocomputing 507 (2022)
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)
Christoph Raab, Manuel Röder, Frank-Michael Schleif
Domain adversarial tangent subspace alignment for explainable domain adaptation. Neurocomputing 506 (2022)
Simon Heilig, Maximilian Münch, Frank-Michael Schleif
Memory Efficient Kernel Approximation for Non-Stationary and Indefinite Kernels. IJCNN (2022)
Moritz Heusinger, Christoph Raab, Frank-Michael Schleif
Dimensionality reduction in the context of dynamic social media data streams. Evol. Syst. 13 (3) (2022)
Moritz Heusinger, Christoph Raab, Frank-Michael Schleif
Passive concept drift handling via variations of learning vector quantization. Neural Comput. Appl. 34 (1) (2022)
Moritz Heusinger, Christoph Raab, Fabrice Rossi, Frank-Michael Schleif
Federated Learning - Methods, Applications and beyond. CoRR (2022)
Christoph Raab, Sascha Saralajew, Frank-Michael Schleif
Domain Adversarial Tangent Learning Towards Interpretable Domain Adaptation. ESANN (2021)
Maximilian Münch, Simon Heilig, Frank-Michael Schleif
Multi-perspective embedding for non-metric time series classification. ESANN (2021)
Simon Heilig, Maximilian Münch, Frank-Michael Schleif
Revisiting Memory Efficient Kernel Approximation: An Indefinite Learning Perspective. CoRR (2021)
Moritz Heusinger, Christoph Raab, Fabrice Rossi, Frank-Michael Schleif
Federated Learning - Methods, Applications and beyond. ESANN (2021)
Maximilian Münch, Simon Heilig, Philipp Väth, Frank-Michael Schleif
Scalable embedding of multiple perspectives for indefinite life-science data analysis. SSCI (2021)
Moritz Heusinger, Frank-Michael Schleif
Classification in Non-stationary Environments Using Coresets over Sliding Windows. IWANN (1) (2021)
Moritz Heusinger, Frank-Michael Schleif
Random Projection in the Presence of Concept Drift in Supervised Environments. ICAISC (1) (2020)
Christoph Raab, Moritz Heusinger, Frank-Michael Schleif
Reactive Soft Prototype Computing for Concept Drift Streams. CoRR (2020)
Christoph Raab, Philipp Väth, Peter Meier, Frank-Michael Schleif
Bridging Adversarial and Statistical Domain Transfer via Spectral Adaptation Networks. ACCV (3) (2020)
Frank-Michael Schleif, Christoph Raab, Peter Tiño
Sparsification of core set models in non-metric supervised learning. Pattern Recognit. Lett. 129 (2020)
Maximilian Münch, Michiel Straat, Michael Biehl, Frank-Michael Schleif
Complex-valued embeddings of generic proximity data. CoRR (2020)
Maximilian Münch, Christoph Raab, Michael Biehl, Frank-Michael Schleif
Data-Driven Supervised Learning for Life Science Data. Frontiers Appl. Math. Stat. 6 (2020)
Christoph Raab, Frank-Michael Schleif
Low-Rank Subspace Override for Unsupervised Domain Adaptation. KI (2020)
Christoph Raab, Moritz Heusinger, Frank-Michael Schleif
Reactive Soft Prototype Computing for Concept Drift Streams. Neurocomputing 416 (2020)
Moritz Heusinger, Frank-Michael Schleif
Reactive Concept Drift Detection Using Coresets Over Sliding Windows. SSCI (2020)
Maximilian Münch, Christoph Raab, Michael Biehl, Frank-Michael Schleif
Structure Preserving Encoding of Non-euclidean Similarity Data. ICPRAM (2020)
Christoph Raab, Frank-Michael Schleif
Transfer learning extensions for the probabilistic classification vector machine. CoRR (2020)
Moritz Heusinger, Frank-Michael Schleif
Random Projection in supervised non-stationary environments. ESANN (2020)
Maximilian Münch, Michiel Straat, Michael Biehl, Frank-Michael Schleif
Complex-Valued Embeddings of Generic Proximity Data. S+SSPR (2020)
Christoph Raab, Frank-Michael Schleif
Transfer learning extensions for the probabilistic classification vector machine. Neurocomputing 397 (2020)
Maximilian Münch, Christoph Raab, Frank-Michael Schleif
Encoding of Indefinite Proximity Data: A Structure Preserving Perspective. ICPRAM (Revised Selected Papers) (2020)
Christoph Raab, Peter Meier, Frank-Michael Schleif
Domain Invariant Representations with Deep Spectral Alignment. ESANN (2020)
Moritz Heusinger, Christoph Raab, Frank-Michael Schleif
Analyzing Dynamic Social Media Data via Random Projection - A New Challenge for Stream Classifiers. EAIS (2020)
Albert Bifet, Barbara Hammer, Frank-Michael Schleif
Recent trends in streaming data analysis, concept drift and analysis of dynamic data sets. ESANN (2019)
Christoph Raab, Moritz Heusinger, Frank-Michael Schleif
Reactive Soft Prototype Computing for frequent reoccurring Concept Drift. ESANN (2019)
Christoph Raab, Frank-Michael Schleif
Domain Adaptation via Low-Rank Basis Approximation. CoRR (2019)
Maximilian Münch, Karsten Huffstadt, Frank-Michael Schleif
Towards a device-free passive presence detection system with Bluetooth Low Energy beacons. ESANN (2019)
Moritz Heusinger, Christoph Raab, Frank-Michael Schleif
Passive Concept Drift Handling via Momentum Based Robust Soft Learning Vector Quantization. WSOM+ (2019)
Mohammad Mohammadi, Nicolai Petkov, Kerstin Bunte, Reynier F. Peletier, Frank-Michael Schleif
Globular cluster detection in the GAIA survey. Neurocomputing 342 (2019)
Maximilian Münch, Frank-Michael Schleif
Device-Free Passive Human Counting with Bluetooth Low Energy Beacons. IWANN (2) (2019)
Luca Oneto, Kerstin Bunte, Frank-Michael Schleif
European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2018). Neurocomputing 342 (2019)
Christoph Raab, Frank-Michael Schleif
Sparse Transfer Classification for Text Documents. KI (2018)
Frank-Michael Schleif, Andrej Gisbrecht, Peter Tiño
Supervised low rank indefinite kernel approximation using minimum enclosing balls. Neurocomputing 318 (2018)
Christoph Raab, Frank-Michael Schleif
Transfer learning for the probabilistic classification vector machine. COPA (2018)
Mohammad Mohammadi, Reynier Peletier, Frank-Michael Schleif, Nicolai Petkov, Kerstin Bunte
Globular Cluster Detection in the Gaia Survey. ESANN (2018)
Frank-Michael Schleif, Christoph Raab, Peter Tiño
Sparsification of Indefinite Learning Models. S+SSPR (2018)
Frank-Michael Schleif
Small sets of random Fourier features by kernelized Matrix LVQ. WSOM (2017)
Frank-Michael Schleif
Indefinite Support Vector Regression. ICANN (2) (2017)
Frank-Michael Schleif, Peter Tiño
Indefinite Core Vector Machine. Pattern Recognit. 71 (2017)
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) (2016)
Kerstin Bunte, Marika Kaden, Frank-Michael Schleif
Low-Rank Kernel Space Representations in Prototype Learning. WSOM (2016)
Frank-Michael Schleif, Peter Tiño, Yingyu Liang
Learning in indefinite proximity spaces - recent trends. ESANN (2016)
Frank-Michael Schleif, Andrej Gisbrecht, Peter Tiño
Probabilistic classifiers with low rank indefinite kernels. CoRR (2016)
Frank-Michael Schleif, Ata Kabán, Peter Tiño
Finding Small Sets of Random Fourier Features for Shift-Invariant Kernel Approximation. ANNPR (2016)
Frank-Michael Schleif, Andrej Gisbrecht, Peter Tiño
Probabilistic Classification Vector Machine at large scale. ESANN (2015)
Andrej Gisbrecht, Frank-Michael Schleif
Metric and non-metric proximity transformations at linear costs. Neurocomputing 167 (2015)
Bassam Mokbel, Benjamin Paaßen, Frank-Michael Schleif, Barbara Hammer
Metric learning for sequences in relational LVQ. Neurocomputing 169 (2015)
Frank-Michael Schleif, H. Chen, Peter Tiño
Incremental probabilistic classification vector machine with linear costs. IJCNN (2015)
Michael Biehl, Barbara Hammer, Frank-Michael Schleif, Petra Schneider, Thomas Villmann
Stationarity of Matrix Relevance LVQ. IJCNN (2015)
Frank-Michael Schleif, Peter Tiño
Indefinite Proximity Learning: A Review. Neural Comput. 27 (10) (2015)
Michael Biehl, Alessandro Ghio, Frank-Michael Schleif
Developments in computational intelligence and machine learning. Neurocomputing 169 (2015)
Frank-Michael Schleif
Generic probabilistic prototype based classification of vectorial and proximity data. Neurocomputing 154 (2015)
Frank-Michael Schleif, Andrej Gisbrecht, Peter Tiño
Large Scale Indefinite Kernel Fisher Discriminant. SIMBAD (2015)
Frank-Michael Schleif, Xibin Zhu, Barbara Hammer
Sparse conformal prediction for dissimilarity data. Ann. Math. Artif. Intell. 74 (1-2) (2015)
Marc Strickert, Kerstin Bunte, Frank-Michael Schleif, Eyke Hüllermeier
Correlation-based embedding of pairwise score data. Neurocomputing 141 (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 (2014)
Xibin Zhu, Frank-Michael Schleif, Barbara Hammer
Adaptive conformal semi-supervised vector quantization for dissimilarity data. Pattern Recognit. Lett. 49 (2014)
Frank-Michael Schleif
Discriminative Fast Soft Competitive Learning. ICANN (2014)
Frank-Michael Schleif, Peter Tiño, Thomas Villmann
Recent trends in learning of structured and non-standard data. ESANN (2014)
Frank-Michael Schleif, Thomas Villmann, Xibin Zhu
High Dimensional Matrix Relevance Learning. ICDM Workshops (2014)
Frank-Michael Schleif
Proximity learning for non-standard big data. ESANN (2014)
Daniela Hofmann, Frank-Michael Schleif, Benjamin Paaßen, Barbara Hammer
Learning interpretable kernelized prototype-based models. Neurocomputing 141 (2014)
Andrej Gisbrecht, Frank-Michael Schleif
Metric and non-metric proximity transformations at linear costs. CoRR (2014)
Barbara Hammer, Daniela Hofmann, Frank-Michael Schleif, Xibin Zhu
Learning vector quantization for (dis-)similarities. Neurocomputing 131 (2014)
Tina Geweniger, Frank-Michael Schleif, Thomas Villmann
Probabilistic Prototype Classification Using t-norms. WSOM (2014)
Xibin Zhu, Frank-Michael Schleif, Barbara Hammer
Secure Semi-supervised Vector Quantization for Dissimilarity Data. IWANN (1) (2013)
Frank-Michael Schleif, Xibin Zhu, Barbara Hammer
Sparse Prototype Representation by Core Sets. IDEAL (2013)
Frank-Michael Schleif, Andrej Gisbrecht
Data Analysis of (Non-)Metric Proximities at Linear Costs. SIMBAD (2013)
Alessio Micheli, Frank-Michael Schleif, Peter Tiño
Novel approaches in machine learning and computational intelligence. Neurocomputing 112 (2013)
Xibin Zhu, Frank-Michael Schleif, Barbara Hammer
Semi-Supervised Vector Quantization for proximity data. ESANN (2013)
Barbara Hammer, Bassam Mokbel, Frank-Michael Schleif, Xibin Zhu
White Box Classification of Dissimilarity Data. HAIS (1) (2012)
Xibin Zhu, Frank-Michael Schleif, Barbara Hammer
Patch Processing for Relational Learning Vector Quantization. ISNN (1) (2012)
Xibin Zhu, Andrej Gisbrecht, Frank-Michael Schleif, Barbara Hammer
Approximation techniques for clustering dissimilarity data. Neurocomputing 90 (2012)
Andrej Gisbrecht, Bassam Mokbel, Frank-Michael Schleif, Xibin Zhu, Barbara Hammer
Linear Time Relational Prototype Based Learning. Int. J. Neural Syst. 22 (5) (2012)
Kerstin Bunte, Frank-Michael Schleif, Michael Biehl
Adaptive learning for complex-valued data. ESANN (2012)
Frank-Michael Schleif, Xibin Zhu, Barbara Hammer
A Conformal Classifier for Dissimilarity Data. AIAI (2) (2012)
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)
Frank-Michael Schleif, Xibin Zhu, Andrej Gisbrecht, Barbara Hammer
Fast approximated relational and kernel clustering. ICPR (2012)
Michael Biehl, Kerstin Bunte, Frank-Michael Schleif, Petra Schneider, Thomas Villmann
Large margin linear discriminative visualization by Matrix Relevance Learning. IJCNN (2012)
Frank-Michael Schleif, Andrej Gisbrecht, Barbara Hammer
Relevance learning for short high-dimensional time series in the life sciences. IJCNN (2012)
Frank-Michael Schleif, Xibin Zhu, Barbara Hammer
Soft Competitive Learning for Large Data Sets. ADBIS Workshops (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 (2012)
Barbara Hammer, Bassam Mokbel, Frank-Michael Schleif, Xibin Zhu
Prototype-Based Classification of Dissimilarity Data. IDA (2011)
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)
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) (2011)
Frank-Michael Schleif
Sparse kernelized vector quantization with local dependencies. IJCNN (2011)

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Publication Activity (10 Years)

Top Topics

Top Venues

Frank-Michael Schleif