Forschungsgebiete Machine Learning, Computational Intelligence, Datenanaylse und Data Miningmit der Ausrichtung:Interpretierbare Künstliche Intelligenz (KI), Integration und Extraktion von Expertenwissen in/aus den KI-Modellen
Projektmitgliedschaft Interpretierbare KI für die Analyse von Gene-Expressionsdaten finanziert durch Bundesministerium für Wirtschaft und Klimaschutz 2022 - 2025 Entwicklung des KI-Demonstrators (AID) zur effizienten Datenverarbeitung in Mikrogravitationsexperimenten finanziert durch Bundesministerium für Bildung und Forschung 2021 - 2023 Nachwuchsforschergruppe MaLeKITA Maschinelles Lernen und KI in Theorie und Anwendungen, MaLeKITA Anwendungen Theorie finanziert durch Sächsische Aufbaubank 2020 - 2022 Repräsentation von Molekülen in für maschinelles Lernen geeigneten Datenstrukturen und Vorhersage von Moleküleigenschaften 2020 - 2021 Nutzung von Methoden der Künstlichen Intelligenz und des maschinellen Lernens mit alaska-Softwareproduktion für die Simulation mechatronischer Systeme und Mensch-Technik-Interaktion finanziert durch 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 2018 - 2019
ausgewählte Publikationen Journalartikel Alignment-free sequence comparison: A systematic survey from a machine learning perspective. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 1-1. 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 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 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
Organisation von Veranstaltungen 4th International Workshop on Bioinformatics meets Machine Learning 2020 12th Mittweida Workshop in Computational Intelligence 2020 2020 Artificial Intelligence meets Industry & Business 2020
bearbeitet/e wissenschaftliche Arbeit Promotion Promotion von Marika Kaden betreut von Villmann, Thomas