Kaden, Marika

Dr. rer. nat.

Positionen

Mitarbeiterin, Fachgruppe Mathematik, Fakultät Angewandte Computer- und Biowissenschaften

Übersicht zur Forschung

Künstliche Intelligenz (KI), Machienelles Lernen, Computational Intelligence, Datenanalyse und Data Mining

mit der Ausrichtung:
Interpretierbare Künstliche Intelligenz,
Integration und Extraktion von Expertenwissen in/aus den KI-Modellen

Forschungsschwerpunkt

Angewandte Informatik Forschungsschwerpunkt

Leitung von

Entwicklung des KI-Demonstrators (AID) zur effizienten Datenverarbeitung in Mikrogravitationsexperimenten gefördert von Deutsches Zentrum für Luft- und Raumfahrt e.V. 2023 - 2026
Interpretierbare KI für die Analyse von Gene-Expressionsdaten gefördert von Bundesministerium für Wirtschaft und Klimaschutz 2022 - 2025
Entwicklung des KI-Demonstrators (AID) zur effizienten Datenverarbeitung in Mikrogravitationsexperimenten gefördert von Bundesministerium für Bildung und Forschung 2021 - 2023
Nachwuchsforschergruppe MaLeKITA Maschinelles Lernen und KI in Theorie und Anwendungen, MaLeKITA Anwendungen Theorie gefördert von Sächsische Aufbaubank 2020 - 2022
Repräsentation von Molekülen in für maschinelles Lernen geeigneten Datenstrukturen und Vorhersage von Moleküleigenschaften gefördert von PharmAI GmbH 2020 - 2021
Nutzung von Methoden der Künstlichen Intelligenz und des maschinellen Lernens mit alaska-Softwareproduktion für die Simulation mechatronischer Systeme und Mensch-Technik-Interaktion gefördert von Sächsisches Staatsministerium für Wissenschaft, Kultur und Tourismus 2019 - 2021
Weiterentwicklung von Algorithmen des maschinellen Lernens zur Analyse von Daten der LKS und dem damit verbundenen Wissenserwerb gefördert von Landwirtschaftliche Kommunikations- und Servicegesellschaft mbH 2018 - 2019

ausgewählte Veröffentlichungen

Journalartikel
- Multi-proximity based embedding scheme for learning vector quantization-based classification of biochemical structured data. Neurocomputing. 126632. 2023
- Alignment-free sequence comparison: A systematic survey from a machine learning perspective. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 119-135. 2022
- Variants of recurrent learning vector quantization. Neurocomputing. 27-36. 2022
- AI-Based Multi Sensor Fusion for Smart Decision Making: A Bi-Functional System for Single Sensor Evaluation in a Classification Task. Sensors. 4405. 2021
- Learning vector quantization as an interpretable classifier for the detection of SARS-CoV-2 types based on their RNA sequences. Neural Computing and Applications. 2021
- RecLVQ: Recurrent Learning Vector Quantization. ESANN 2021 - proceedings. 2021
- The LVQ-based Counter Propagation Network -- an Interpretable Information Bottleneck Approach. ESANN 2021 - proceedings. 2021
- The Resolved Mutual Information Function as a Structural Fingerprint of Biomolecular Sequences for Interpretable Machine Learning Classifiers. Entropy. 1357. 2021
- Analysis of SARS-CoV-2 RNA-Sequences by Interpretable Machine Learning Models. bioRxiv. 2020
- Detection of native and mirror protein structures based on Ramachandran plot analysis by interpretable machine learning models. bioRxiv. 2020
- Investigation of Activation Functions for Generalized Learning Vector Quantization. Advances in Intelligent Systems and Computing 2020. 179-188. 2020
- Learning vector quantization and relevances in complex coefficient space. Neural Computing and Applications. 18085-18099. 2020
- Searching for the Origins of Life – Detecting RNA Life Signatures Using Learning Vector Quantization. Advances in Intelligent Systems and Computing 2020. 324-333. 2020
- Variants of DropConnect in Learning vector quantization networks for evaluation of classification stability. Neurocomputing. 121-132. 2020
- Activation Functions for Generalized Learning Vector Quantization - A Performance Comparison. arXiv. 2019
- Application of an interpretable classification model on Early Folding Residues during protein folding. BioData Mining. 1. 2019
- Appropriate Data Density Models in Probabilistic Machine Learning Approaches for Data Analysis. Artificial Intelligence and Soft Computing 2019. 443-454. 2019
- Learning vector quantization classifiers for ROC-optimization. Computational Statistics. 1173-1194. 2018
- Probabilistic Learning Vector Quantization with Cross-Entropy for Probabilistic Class Assignments in Classification Learning. Artificial Intelligence and Soft Computing 2018. 724-735. 2018
- Can Learning Vector Quantization be an Alternative to SVM and Deep Learning? - Recent Trends and Advanced Variants of Learning Vector Quantization for Classification Learning. Journal of Artificial Intelligence and Soft Computing Research. 65-81. 2017
- Border-sensitive learning in generalized learning vector quantization: an alternative to support vector machines. Soft Computing. 2423-2434. 2015
- Data similarities, dissimilarities and types of inner products - a mathematical characterization in the context of machine learning. Machine Learning Reports. 18-28. 2015
- Kernelized Vector Quantization in Gradient-Descent Learning. Neurocomputing. 83-85. 2015
Konferenzpaper
- Biologically-Informed Shallow Classification Learning Integrating Pathway Knowledge. Proceedings of the 17th International Joint Conference on Biomedical Engineering Systems and Technologies - BIOINFORMATICS. 357-367. 2024
- A White-Box Workflow for the Prediction of Food Content From Near-Infrared Data Based on Fourier-Transformation. 2023 13th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS). 2023
- Compression of Particle Images for Inspection of Microgravity Experiments by Means of a Symmetric Structural Auto-Encoder. 2023 13th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS). 2023
- Efficient Representation of Biochemical Structures for Supervised and Unsupervised Machine Learning Models Using Multi-Sensoric Embeddings. Proceedings of the 16th International Joint Conference on Biomedical Engineering Systems and Technologies - BIOINFORMATICS. 59-69. 2023
- Trustworthiness and Confidence of Gait Phase Predictions in Changing Environments Using Interpretable Classifier Models. Neural Information Processing. 379-390. 2023
- Variants of Neural Gas for Regression Learning. ESANN 2023 Proceedings. European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. 659-664. 2023
- A Learning Vector Quantization Architecture for Transfer Learning Based Classification in Case of Multiple Sources by Means of Null-Space Evaluation. Lecture Notes in Computer Science. 354-364. 2022
- Efficient classification learning of biochemical structured data by means of relevance weighting for sensoric response features. ESANN 2022 Proceedings. European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. 445-450. 2022
- Prototype-based One-Class-Classification Learning Using Local Representations. 2022 International Joint Conference on Neural Networks (IJCNN). 2022
- Intelligent Gait Analysis using Marker Based Motion Capturing System. Scientific Reports 2021, Ökologische Transformation in Technik, Wirtschaft und Gesellschaft?. 133-136. 2021
- Sensors Data Fusion for Smart Decisions Making Using Interpretative Machine Learning Models. Scientific Reports 2021, Ökologische Transformation in Technik, Wirtschaft und Gesellschaft?. 145-146. 2021
- Sensors data fusion for smart decisions making: A novel bi-functional system for the evaluation of sensors contribution in classification problems. 2021 22nd IEEE International Conference on Industrial Technology (ICIT). 2021
Sammelbandbeitrag
- Quantum-inspired learning vector quantization for classification learning. Proceedings of the 28th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN'2020). 2020
- Investigating the Influence of CPU Load, Memory Usage and Environmental Conditions on the Jittering of Android Devices. Proceedings of the 2018 VII International Conference on Network, Communication and Computing. 102-106. 2018
- Methoden des maschinellen Lernens und der Computational Intelligence zur Auswertung heterogener Daten in der digitalen Forensik. Forensik in der digitalen Welt. 239-263. 2017
- Complex Variants of GLVQ Based on Wirtinger’s Calculus. Advances in Self-Organizing Maps and Learning Vector Quantization, Proceedings of the 11th International Workshop WSOM 2016. 293-303. 2016
- Similarities, Dissimilarities and Types of Inner Products for Data Analysis in the Context of Machine Learning. Artificial Intelligence and Soft Computing 2016. 125-133. 2016
- Characterizing Protein functions: large scale Screening and classification of structural motifs. 3DSIG - Structural Bioinformatics and Computational Biophysics. 2015
- Learning Matrix Quantization and Variants of Relevance Learning. Proceedings of the European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2015). 13-18. 2015
- Learning Vector Quantization with Adaptive Cost-Based Outlier-Rejection. Computer Analysis of Images and Patterns. 772-782. 2015

Ausrichter des Events

bearbeitet/e wissenschaftliche Arbeit

Promotion
- Promotion von Marika Kaden betreut von Villmann, Thomas

vollständiger Name

Marika Kaden

zugehörig zur Einrichtung

6-120

KoautorInnen-Netzwerk

Koforscher-Netzwerk

Kontaktdaten

kaden1@hs-mittweida.de

+49 3727 581516