Tutorial - Machine Learning and Information Theoretic Methods for Molecular Biology and Medicine.
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ESANN |
2022 |
0 |
Efficient classification learning of biochemical structured data by means of relevance weighting for sensoric response features.
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ESANN |
2022 |
0 |
Prototype-based One-Class-Classification Learning Using Local Representations.
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IJCNN |
2022 |
0 |
RecLVQ: Recurrent Learning Vector Quantization.
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ESANN |
2021 |
0 |
The LVQ-based Counter Propagation Network - an Interpretable Information Bottleneck Approach.
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ESANN |
2021 |
1 |
The Coming of Age of Interpretable and Explainable Machine Learning Models.
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ESANN |
2021 |
3 |
Quantum-Inspired Learning Vector Quantization for Classification Learning.
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ESANN |
2020 |
4 |
Fast Adversarial Robustness Certification of Nearest Prototype Classifiers for Arbitrary Seminorms.
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NIPS/NeurIPS |
2020 |
12 |
Statistical physics of learning and inference.
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ESANN |
2019 |
3 |
Classification-by-Components: Probabilistic Modeling of Reasoning over a Set of Components.
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NIPS/NeurIPS |
2019 |
18 |
DropConnect for Evaluation of Classification Stability in Learning Vector Quantization.
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ESANN |
2019 |
3 |
Reliable Patient Classification in Case of Uncertain Class Labels Using a Cross-Entropy Approach.
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ESANN |
2018 |
2 |
Transfer learning in classification based on manifolc. models and its relation to tangent metric learning.
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IJCNN |
2017 |
6 |
Biomedical data analysis in translational research: integration of expert knowledge and interpretable models.
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ESANN |
2017 |
9 |
Adaptive tangent distances in generalized learning vector quantization for transformation and distortion invariant classification learning.
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IJCNN |
2016 |
2 |
Adaptive dissimilarity weighting for prototype-based classification optimizing mixtures of dissimilarities.
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ESANN |
2016 |
6 |
Median-LVQ for classification of dissimilarity data based on ROC-optimization.
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ESANN |
2015 |
1 |
Learning matrix quantization and variants of relevance learning.
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ESANN |
2015 |
4 |
Stationarity of Matrix Relevance LVQ.
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IJCNN |
2015 |
22 |
Recent trends in learning of structured and non-standard data.
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ESANN |
2014 |
0 |
Supervised Generative Models for Learning Dissimilarity Data.
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ESANN |
2014 |
6 |
Utilization of Chemical Structure Information for Analysis of Spectra Composites.
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ESANN |
2014 |
2 |
Optimization of General Statistical Accuracy Measures for Classification Based on Learning Vector Quantization.
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ESANN |
2014 |
12 |
Applications of lp-Norms and their Smooth Approximations for Gradient Based Learning Vector Quantization.
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ESANN |
2014 |
50 |
About analysis and robust classification of searchlight fMRI-data using machine learning classifiers.
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IJCNN |
2013 |
1 |
Processing Hyperspectral Data in Machine Learning.
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ESANN |
2013 |
10 |
Non-Euclidean independent component analysis and Oja's learning.
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ESANN |
2013 |
4 |
Border sensitive fuzzy vector quantization in semi-supervised learning.
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ESANN |
2013 |
2 |
Regularization in relevance learning vector quantization using l1-norms.
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ESANN |
2013 |
6 |
A sparse kernelized matrix learning vector quantization model for human activity recognition.
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ESANN |
2013 |
33 |
Large margin linear discriminative visualization by Matrix Relevance Learning.
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IJCNN |
2012 |
19 |
Integration of Structural Expert Knowledge about Classes for Classification Using the Fuzzy Supervised Neural Gas.
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ESANN |
2012 |
6 |
Recent developments in clustering algorithms.
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ESANN |
2012 |
12 |
Modified Conn-Index for the evaluation of fuzzy clusterings.
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ESANN |
2012 |
4 |
Unmixing Hyperspectral Images with Fuzzy Supervised Self-Organizing Maps.
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ESANN |
2012 |
5 |
Optimization of Parametrized Divergences in Fuzzy c-Means.
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ESANN |
2011 |
6 |
Mathematical Foundations of the Self Organized Neighbor Embedding (SONE) for Dimension Reduction and Visualization.
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ESANN |
2011 |
8 |
Multispectral image characterization by partial generalized covariance.
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ESANN |
2011 |
4 |
Information theory related learning.
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ESANN |
2011 |
5 |
Multivariate class labeling in Robust Soft LVQ.
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ESANN |
2011 |
9 |
Generalized functional relevance learning vector quantization.
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ESANN |
2011 |
11 |
Magnification in divergence based neural maps.
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IJCNN |
2011 |
3 |
Exploratory Observation Machine (XOM) with Kullback-Leibler Divergence for Dimensionality Reduction and Visualization.
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ESANN |
2010 |
57 |
Sparse representation of data.
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ESANN |
2010 |
2 |
Divergence based Learning Vector Quantization.
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ESANN |
2010 |
19 |
Extending FSNPC to handle data points with fuzzy class assignments.
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ESANN |
2010 |
4 |
Regularization in matrix relevance learning.
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IEEE Trans. Neural Networks |
2010 |
91 |
Learning vector quantization for heterogeneous structured data.
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ESANN |
2010 |
11 |
Neural Maps and Learning Vector Quantization - Theory and Applications.
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ESANN |
2009 |
1 |
Fuzzy Fleiss-kappa for Comparison of Fuzzy Classifiers.
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ESANN |
2009 |
7 |
Median Variant of Fuzzy c-Means.
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ESANN |
2009 |
4 |
Magnification Control in Relational Neural Gas.
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ESANN |
2008 |
5 |
Generalized matrix learning vector quantizer for the analysis of spectral data.
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ESANN |
2008 |
17 |
Metric adaptation for supervised attribute rating.
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ESANN |
2008 |
1 |
Machine learning approches and pattern recognition for spectral data.
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ESANN |
2008 |
13 |
How to process uncertainty in machine learning?.
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ESANN |
2007 |
22 |
Intuitive Clustering of Biological Data.
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IJCNN |
2007 |
4 |
Visualization of Fuzzy Information in Fuzzy-Classification for Image Segmentation using MDS.
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ESANN |
2007 |
12 |
Explicit Magnification Control of Self-Organizing Maps for "Forbidden" Data.
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IEEE Trans. Neural Networks |
2007 |
0 |
Neural networks and machine learning in bioinformatics - theory and applications.
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ESANN |
2006 |
23 |
Fuzzy image segmentation with Fuzzy Labelled Neural Gas.
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ESANN |
2006 |
16 |
Magnification control for batch neural gas.
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ESANN |
2006 |
0 |
Margin based Active Learning for LVQ Networks.
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ESANN |
2006 |
0 |
Classification using non-standard metrics.
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ESANN |
2005 |
24 |
Relevance learning for mental disease classification.
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ESANN |
2005 |
3 |
Generalized Relevance LVQ with Correlation Measures for Biological Data.
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ESANN |
2005 |
4 |
Theory and applications of neural maps.
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ESANN |
2004 |
13 |
Mathematical Aspects of Neural Networks.
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ESANN |
2003 |
41 |
Magnification Control in Winner Relaxing Neural Gas.
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ESANN |
2003 |
0 |
Batch-RLVQ.
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ESANN |
2002 |
4 |
Rule Extraction from Self-Organizing Networks.
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ICANN |
2002 |
29 |
Exploratory Data Analysis in Medicine and Bioinformatics.
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ESANN |
2002 |
2 |
Learning Vector Quantization for Multimodal Data.
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ICANN |
2002 |
18 |
Input pruning for neural gas architectures.
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ESANN |
2001 |
4 |
Evolutionary algorithms and neural networks in hybrid systems.
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ESANN |
2001 |
12 |
Parallel Evolutionary Algorithms with SOM-Like Migration and its Application to VLSI-Design.
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IJCNN |
2000 |
4 |
Neural networks approaches in medicine - a review of actual developments.
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ESANN |
2000 |
13 |
Benefits and limits of the self-organizing map and its variants in the area of satellite remote sensoring processing.
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ESANN |
1999 |
15 |
Magnification control in neural maps.
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ESANN |
1998 |
14 |
Measuring topology preservation in maps of real-world data.
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ESANN |
1997 |
8 |
Vector Quantization by Optimal Neural Gas.
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ICANN |
1997 |
32 |
Growing a hypercubical output space in a self-organizing feature map.
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IEEE Trans. Neural Networks |
1997 |
150 |
Topology preservation in self-organizing feature maps: exact definition and measurement.
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IEEE Trans. Neural Networks |
1997 |
356 |