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Integration of Auxiliary Data Knowledge in Prototype Based Vector Quantization and Classification Models. Leipzig University, Germany, (2016)base-search.net (ftunivleipzig:oai:qucosa:de:qucosa:14833).Quantum-Inspired Learning Vector Quantization for Classification Learning., , , , и . ESANN, стр. 279-284. (2020)Compression of Particle Images for Inspection of Microgravity Experiments by Means of a Symmetric Structural Auto-Encoder., , , , и . WHISPERS, стр. 1-5. IEEE, (2023)Investigating the Influence of CPU Load, Memory Usage and Environmental Conditions on the Jittering of Android Devices., , , , , , и . ICNCC, стр. 102-106. ACM, (2018)Efficient classification learning of biochemical structured data by means of relevance weighting for sensoric response features., , , и . ESANN, (2022)Investigation of Activation Functions for Generalized Learning Vector Quantization., , , , и . WSOM+, том 976 из Advances in Intelligent Systems and Computing, стр. 179-188. Springer, (2019)Possibilistic Classification Learning Based on Contrastive Loss in Learning Vector Quantizer Networks., , и . ICAISC (1), том 12854 из Lecture Notes in Computer Science, стр. 156-167. Springer, (2021)Searching for the Origins of Life - Detecting RNA Life Signatures Using Learning Vector Quantization., , , , , , и . WSOM+, том 976 из Advances in Intelligent Systems and Computing, стр. 324-333. Springer, (2019)Quantum-inspired learning vector quantizers for prototype-based classification., , , , и . Neural Comput. Appl., 34 (1): 79-88 (2022)Appropriate Data Density Models in Probabilistic Machine Learning Approaches for Data Analysis., , , и . ICAISC (2), том 11509 из Lecture Notes in Computer Science, стр. 443-454. Springer, (2019)