Frank-Michael Schleif

发表

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

Thomas Villmann, Frank-Michael Schleif, Matthias Ongyerth, 2009, Neurocomputing.

Advances in Self-Organizing Maps and Learning Vector Quantization - Proceedings of the 10th International Workshop, WSOM 2014, Mittweida, Germany, July, 2-4, 2014

Thomas Villmann, Frank-Michael Schleif, Marika Kaden, 2014, WSOM.

Data-Driven Supervised Learning for Life Science Data

Michael Biehl, Frank-Michael Schleif, Maximilian Münch, 2020, Frontiers in Applied Mathematics and Statistics.

Encoding of Indefinite Proximity Data: A Structure Preserving Perspective

Frank-Michael Schleif, Maximilian Münch, Christoph Raab, 2020, ICPRAM.

Memory Efficient Kernel Approximation for Non-Stationary and Indefinite Kernels

F. Schleif, Maximilian Münch, Simon Heilig, 2022, 2022 International Joint Conference on Neural Networks (IJCNN).

Structure Preserving Encoding of Non-euclidean Similarity Data

Michael Biehl, Frank-Michael Schleif, Christoph Raab, 2020, ICPRAM.

Indefinite Core Vector Machine

Peter Tiño, Frank-Michael Schleif, P. Tiňo, 2017, Pattern Recognit..

Indefinite Proximity Learning: A Review

Peter Tiño, Frank-Michael Schleif, P. Tiňo, 2015, Neural Computation.

Metric and non-metric proximity transformations at linear costs pdf

Frank-Michael Schleif, Andrej Gisbrecht, A. Gisbrecht, 2014, Neurocomputing.

Sparsification of core set models in non-metric supervised learning

Frank-Michael Schleif, Peter Tino, Christoph Raab, 2020, Pattern Recognit. Lett..

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

Frank-Michael Schleif, Maximilian Münch, Karsten Huffstadt, 2019, ESANN.

Globular Cluster Detection in the Gaia Survey

N. Petkov, F. M. Schleif, M. Mohammadi, 2018, ESANN.

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

Thomas Villmann, Frank-Michael Schleif, Sven Haase, 2011, ESANN.

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

Frank-Michael Schleif, T. Riemer, U. Börner, 2011, Bioinform..

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

Thomas Villmann, Frank-Michael Schleif, Barbara Hammer, 2007, Briefings Bioinform..

MiWoCI Workshop-2020

T. Villmann, M. Kaden, F. Schleif, 2020 .

Transfer learning extensions for the probabilistic classification vector machine pdf

Frank-Michael Schleif, Christoph Raab, Christoph Raab, 2020, Neurocomputing.

Device-Free Passive Human Counting with Bluetooth Low Energy Beacons

Frank-Michael Schleif, Maximilian Münch, Maximilian Münch, 2019, IWANN.

Large margin linear discriminative visualization by Matrix Relevance Learning

Thomas Villmann, Michael Biehl, Frank-Michael Schleif, 2012, The 2012 International Joint Conference on Neural Networks (IJCNN).

Support vector classification of proteomic profile spectra based on feature extraction with the bi-orthogonal discrete wavelet transform

Frank-Michael Schleif, Thomas Elssner, Peter Maaß, 2009 .

Sparse kernelized vector quantization with local dependencies

Frank-Michael Schleif, F. Schleif, Frank-Michael Schleif, 2011, The 2011 International Joint Conference on Neural Networks.

Association Learning in SOMs for Fuzzy-Classification

Thomas Villmann, Frank-Michael Schleif, Martijn van der Werff, 2007, Sixth International Conference on Machine Learning and Applications (ICMLA 2007).

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

Maximilian Münch, Philipp Väth, Simon Heilig, 2021, 2021 IEEE Symposium Series on Computational Intelligence (SSCI).

Sparse conformal prediction for dissimilarity data

B. Hammer, Xibin Zhu, Frank-Michael Schleif, 2015, Annals of Mathematics and Artificial Intelligence.

A Conformal Classifier for Dissimilarity Data

Frank-Michael Schleif, Xibin Zhu, Barbara Hammer, 2012, AIAI.

PROVAL: A framework for comparison of protein sequence embeddings

F. Schleif, Maximilian Münch, Philipp Väth, 2022, Journal of Computational Mathematics and Data Science.

Odor recognition in robotics applications by discriminative time-series modeling

Javier Gonzalez Monroy, Javier González, Michael Biehl, 2015, Pattern Analysis and Applications.

Hierarchical Deconvolution of Linear Mixtures of High-Dimensional Mass Spectra in Microbiology

T. Villmann, Frank-Michael Schleif, Stephan Simmuteit, 2011 .

Secure Semi-supervised Vector Quantization for Dissimilarity Data

Frank-Michael Schleif, Xibin Zhu, Barbara Hammer, 2013, IWANN.

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

Thomas Villmann, Frank-Michael Schleif, Barbara Hammer, 2010, Knowledge and Information Systems.

Prototype Based Classification Using Information Theoretic Learning

Thomas Villmann, Frank-Michael Schleif, Barbara Hammer, 2006, ICONIP.

Discriminative visualization by limited rank matrix learning

Thomas Villmann, Michael Biehl, Frank-Michael Schleif, 2008 .

Hierarchical evolving trees together with global and local learning for large data sets in MALDI imaging

Frank-Michael Schleif, T. Villmann, Stephan Simmuteit, 2010 .

Margin based Active Learning for LVQ Networks

Thomas Villmann, Frank-Michael Schleif, Barbara Hammer, 2007, ESANN.

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

Thomas Villmann, Frank-Michael Schleif, Barbara Hammer, 2008, Computational Intelligence in Biomedicine and Bioinformatics.

Stationarity of Matrix Relevance LVQ

Thomas Villmann, Michael Biehl, Frank-Michael Schleif, 2015, 2015 International Joint Conference on Neural Networks (IJCNN).

Topographic Mapping of Dissimilarity Data

Frank-Michael Schleif, Xibin Zhu, Barbara Hammer, 2011, WSOM.

Efficient Cross-Domain Federated Learning by MixStyle Approximation

Maximilian Münch, Manuel Röder, Frank-Michael Schleif, 2023, ArXiv.

Divergence-based classification in learning vector quantization

Thomas Villmann, Michael Biehl, Frank-Michael Schleif, 2011, Neurocomputing.

Divergence Based Online Learning in Vector Quantization

Thomas Villmann, Frank-Michael Schleif, Barbara Hammer, 2010, ICAISC.

The Mathematics of Divergence Based Online Learning in Vector Quantization

Thomas Villmann, Michael Biehl, Frank-Michael Schleif, 2010, ANNPR.

Fuzzy classification using information theoretic learning vector quantization

Thomas Villmann, Frank-Michael Schleif, Barbara Hammer, 2008, Neurocomputing.

Divergence based Learning Vector Quantization

Thomas Villmann, Michael Biehl, Frank-Michael Schleif, 2010, ESANN.

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

Thomas Villmann, Frank-Michael Schleif, Marc Strickert, 2008, Inteligencia Artif..

Statistical Classification and Visualization of MALDI-Imaging Data

Frank-Michael Schleif, Marc Gerhard, Soren-Oliver Deininger, 2007, Twentieth IEEE International Symposium on Computer-Based Medical Systems (CBMS'07).

Reactive Soft Prototype Computing for Concept Drift Streams

Frank-Michael Schleif, Christoph Raab, Moritz Heusinger, 2020, Neurocomputing.

Dimensionality reduction in the context of dynamic social media data streams

Frank-Michael Schleif, Christoph Raab, Moritz Heusinger, 2021, Evolving Systems.

Random Projection in the Presence of Concept Drift in Supervised Environments

Frank-Michael Schleif, Moritz Heusinger, F. Schleif, 2020, ICAISC.

Sparse representation of data

Thomas Villmann, Frank-Michael Schleif, Barbara Hammer, 2010, ESANN.

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

Barbara Hammer, Albert Bifet, Frank-M. Schleif, 2019, ESANN.

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

Frank-Michael Schleif, Moritz Heusinger, Christoph Raab, 2020, 2020 IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS).