Marika Kaden

发表

Application of an interpretable classification model on Early Folding Residues during protein folding

Thomas Villmann, Sebastian Bittrich, Marika Kaden, 2018, bioRxiv.

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.

Border-Sensitive Learning in Kernelized Learning Vector Quantization

Thomas Villmann, Martin Riedel, Marc Strickert, 2013, IWANN.

Optimization of General Statistical Accuracy Measures for Classification Based on Learning Vector Quantization

Thomas Villmann, Wieland Hermann, Marika Kaden, 2014, ESANN.

Low-Rank Kernel Space Representations in Prototype Learning

Frank-Michael Schleif, Kerstin Bunte, Marika Kaden, 2016, WSOM.

Statistical Quality Measures and ROC-Optimization by Learning Vector Quantization Classifiers

Thomas Villmann, Michael Biehl, Marika Kaden, 2014 .

Activation Functions for Generalized Learning Vector Quantization - A Performance Comparison pdf

Thomas Villmann, Andrea Villmann, David Nebel, 2019, ArXiv.

Application of an interpretable classification model on Early Folding Residues during protein folding

Thomas Villmann, Sebastian Bittrich, Marika Kaden, 2018, BioData Mining.

Aspects in Classification Learning - Review of Recent Developments in Learning Vector Quantization

Thomas Villmann, Martin Riedel, Tina Geweniger, 2014 .

Can Learning Vector Quantization be an Alternative to SVM and Deep Learning? - Recent Trends and Advanced Variants of Learning Vector Quantization for Classification Learning

Thomas Villmann, Marika Kaden, Andrea Bohnsack, 2017, J. Artif. Intell. Soft Comput. Res..

Variants of DropConnect in Learning vector quantization networks for evaluation of classification stability

Thomas Villmann, Marika Kaden, Sascha Saralajew, 2020, Neurocomputing.

Attention Based Classification Learning in GLVQ and Asymmetric Misclassification Assessment

Thomas Villmann, Wieland Hermann, Marika Kaden, 2014, WSOM.

Investigating the Influence of CPU Load, Memory Usage and Environmental Conditions on the Jittering of Android Devices

Martin Gaedke, Frederic Ringsleben, Maik Benndorf, 2018, ICNCC 2018.

Reliable Patient Classification in Case of Uncertain Class Labels Using a Cross-Entropy Approach

Thomas Villmann, Andrea Villmann, Wieland Hermann, 2018, ESANN.

Functional relevance learning in generalized learning vector quantization

Thomas Villmann, Michael Biehl, Barbara Hammer, 2012, Neurocomputing.

Border sensitive fuzzy vector quantization in semi-supervised learning

Thomas Villmann, Tina Geweniger, Marika Kaden, 2013, ESANN.

Self-Adjusting Reject Options in Prototype Based Classification

Thomas Villmann, Barbara Hammer, Marika Kaden, 2016, WSOM.

Learning matrix quantization and variants of relevance learning

Thomas Villmann, Marika Kaden, Mandy Lange, 2015, ESANN.

Differentiable Kernels in Generalized Matrix Learning Vector Quantization

Thomas Villmann, Michael Biehl, Martin Riedel, 2012, 2012 11th International Conference on Machine Learning and Applications.

Lateral enhancement in adaptive metric learning for functional data

Thomas Villmann, Martin Riedel, David Nebel, 2014, Neurocomputing.

Non-Euclidean Principal Component Analysis and Oja's Learning Rule - Theoretical Aspects

Thomas Villmann, Michael Biehl, Marika Kaden, 2012, WSOM.

Fuzzy Neural Gas for Unsupervised Vector Quantization

Thomas Villmann, Tina Geweniger, Marika Kaden, 2012, ICAISC.

RFSOM - Extending Self-Organizing Feature Maps with Adaptive Metrics to Combine Spatial and Textural Features for Body Pose Estimation

Thomas Villmann, Sven Hellbach, Martin Riedel, 2014, WSOM.

Types of (dis-)similarities and adaptive mixtures thereof for improved classification learning

Thomas Villmann, Andrea Villmann, David Nebel, 2017, Neurocomputing.

Investigation of Activation Functions for Generalized Learning Vector Quantization

Thomas Villmann, Andrea Villmann, David Nebel, 2019, WSOM+.

Integration of Structural Expert Knowledge about Classes for Classification Using the Fuzzy Supervised Neural Gas

Thomas Villmann, Wieland Hermann, Marika Kaden, 2012, ESANN.

Border-sensitive learning in generalized learning vector quantization: an alternative to support vector machines

Thomas Villmann, Martin Riedel, Wieland Hermann, 2015, Soft Comput..

Sparse Functional Relevance Learning in Generalized Learning Vector Quantization

Thomas Villmann, Marika Kaden, T. Villmann, 2011, WSOM.

Adaptive dissimilarity weighting for prototype-based classification optimizing mixtures of dissimilarities

Thomas Villmann, David Nebel, Marika Kaden, 2016, ESANN.

Modified Conn-Index for the evaluation of fuzzy clusterings

Thomas Villmann, Tina Geweniger, Marika Kaden, 2012, ESANN.

Relevance Learning in Unsupervised Vector Quantization Based on Divergences

Thomas Villmann, Sven Haase, Udo Seiffert, 2011, WSOM.

Data dependent evaluation of dissimilarities in nearest prototype vector quantizers regarding their discriminating abilities

Thomas Villmann, Friedrich Melchert, Udo Seiffert, 2017, 2017 12th International Workshop on Self-Organizing Maps and Learning Vector Quantization, Clustering and Data Visualization (WSOM).

Optimization of Parametrized Divergences in Fuzzy c-Means

Thomas Villmann, Tina Geweniger, Marika Kaden, 2011, ESANN.

Searching for the Origins of Life - Detecting RNA Life Signatures Using Learning Vector Quantization

Thomas Villmann, Jacek Blazewicz, Andrea Villmann, 2019, WSOM+.

Mathematical Characterization of Sophisticated Variants for Relevance Learning in Learning Matrix Quantization Based on Schatten-p-norms

Thomas Villmann, Marika Kaden, Mandy Lange, 2015, ICAISC.

Analysis of SARS-CoV-2 RNA-Sequences by Interpretable Machine Learning Models

Thomas Villmann, Jacek Blazewicz, Marika Kaden, 2020, bioRxiv.

Generalized functional relevance learning vector quantization

Thomas Villmann, Michael Biehl, Barbara Hammer, 2011, ESANN.

Kernelized vector quantization in gradient-descent learning

Thomas Villmann, Sven Haase, Marika Kaden, 2015, Neurocomputing.

Appropriate Data Density Models in Probabilistic Machine Learning Approaches for Data Analysis

Thomas Villmann, Andrea Villmann, Marika Kaden, 2019, ICAISC.

ROC-Optimization and Statistical Quality Measures in Learning Vector Quantization Classifiers

Michael Biehl, T. Villmann, Marika Kaden, 2014 .

Quantum-Inspired Learning Vector Quantization for Classification Learning

Thomas Villmann, Andrea Villmann, Marika Kaden, 2020, ESANN.

Prototypes and matrix relevance learning in complex fourier space

Thomas Villmann, Michael Biehl, Friedrich Melchert, 2017, 2017 12th International Workshop on Self-Organizing Maps and Learning Vector Quantization, Clustering and Data Visualization (WSOM).

Fuzzy Supervised Self-Organizing Map for Semi-supervised Vector Quantization

Thomas Villmann, Marika Kaden, 2012, ICAISC.

Detection of native and mirror protein structures based on Ramachandran plot analysis by interpretable machine learning models

Thomas Villmann, Marika Kaden, Katrin Sophie Bohnsack, 2020 .

Precision-Recall-Optimization in Learning Vector Quantization Classifiers for Improved Medical Classification Systems

Thomas Villmann, Wieland Hermann, Marika Kaden, 2014, 2014 IEEE Symposium on Computational Intelligence and Data Mining (CIDM).

ICMLA Face Recognition Challenge -- Results of the Team Computational Intelligence Mittweida

Thomas Villmann, Martin Riedel, David Nebel, 2012, 2012 11th International Conference on Machine Learning and Applications.

A sparse kernelized matrix learning vector quantization model for human activity recognition

Thomas Villmann, Marc Strickert, Marika Kaden, 2013, ESANN.

About analysis and robust classification of searchlight fMRI-data using machine learning classifiers

Thomas Villmann, Marika Kaden, Mandy Lange, 2013, The 2013 International Joint Conference on Neural Networks (IJCNN).

Quantum-inspired learning vector quantizers for prototype-based classification

Thomas Villmann, Andrea Villmann, Marika Kaden, 2020, Neural Computing and Applications.

Probabilistic Learning Vector Quantization with Cross-Entropy for Probabilistic Class Assignments in Classification Learning

Thomas Villmann, Andrea Villmann, Marika Kaden, 2018, ICAISC.

Complex Variants of GLVQ Based on Wirtinger's Calculus

Thomas Villmann, Michael Biehl, Marika Kaden, 2016, WSOM.

Clustering by Fuzzy Neural Gas and Evaluation of Fuzzy Clusters

Thomas Villmann, Tina Geweniger, Marika Kaden, 2013, Comput. Intell. Neurosci..

Similarities, Dissimilarities and Types of Inner Products for Data Analysis in the Context of Machine Learning - A Mathematical Characterization

Thomas Villmann, David Nebel, Marika Kaden, 2016, ICAISC.

Processing Hyperspectral Data in Machine Learning

Thomas Villmann, Udo Seiffert, Marika Kaden, 2013, ESANN.

Learning Vector Quantization with Adaptive Cost-Based Outlier-Rejection

Thomas Villmann, Michael Biehl, David Nebel, 2015, CAIP.

Gradient Based Learning in Vector Quantization Using Differentiable Kernels

Thomas Villmann, Sven Haase, Marika Kaden, 2012, WSOM.

Regularization in relevance learning vector quantization using l1-norms pdf

Thomas Villmann, Martin Riedel, Fabrice Rossi, 2013, ESANN.

Learning vector quantization classifiers for ROC-optimization

Thomas Villmann, Michael Biehl, Marika Kaden, 2016, Computational Statistics.

The LVQ-based Counter Propagation Network - an Interpretable Information Bottleneck Approach

Thomas Villmann, Marika Kaden, Mehrdad Mohannazadeh Bakhtiari, 2021, ESANN.

RecLVQ: Recurrent Learning Vector Quantization

Thomas Villmann, Marika Kaden, Jensun Ravichandran, 2021, ESANN.

AI-Based Multi Sensor Fusion for Smart Decision Making: A Bi-Functional System for Single Sensor Evaluation in a Classification Task

Thomas Villmann, Germar Schneider, Marika Kaden, 2021, Sensors.

Possibilistic Classification Learning Based on Contrastive Loss in Learning Vector Quantizer Networks

Thomas Villmann, Marika Kaden, Seyedfakhredin Musavishavazi, 2021, ICAISC.

Sensors data fusion for smart decisions making: A novel bi-functional system for the evaluation of sensors contribution in classification problems

Thomas Villmann, Germar Schneider, Marika Kaden, 2021, 2021 22nd IEEE International Conference on Industrial Technology (ICIT).

The Resolved Mutual Information Function as a Structural Fingerprint of Biomolecular Sequences for Interpretable Machine Learning Classifiers

Thomas Villmann, Marika Kaden, Sascha Saralajew, 2021, Entropy.