A.R. Engelsberger, MSc

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a.r.engelsberger rug.nl

Publicaties

FA(IR)2MA-GLVQ – A hidden-feature-bias mitigation approach for fairness in classification learning based on generalized matrix learning vector quantization

Kaden, M., Schubert, R., Voigt, J., Reuss, L., Engelsberger, A., Lövdal, S., van den Brandhof, E. L., Biehl, M. & Villmann, T., 14-mei-2026, In: Neurocomputing. 678, 11 blz., 133200.

Onderzoeksoutput: Article › › peer review

Prototype-Based Classifiers and Vector Quantization on a Quantum Computer: Implementing Integer Arithmetic Oracles for Nearest Prototype Search

Engelsberger, A., Pšeničkova, M. & Villmann, T., 16-feb.-2026, (E-pub ahead of print) In: Entropy. 28, 2, 18 blz., 229.

Onderzoeksoutput: Article › › peer review

Mitigating the Bias in Data for Fairness Using an Advanced Generalized Learning Vector Quantization Approach -- FA(IR)^2MA-GLVQ

Kaden, M., Engelsberger, A., Schubert, R., Lövdal, S., van den Brandhof, E., Biehl, M. & Villmann, T., 25-apr.-2025, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning: ESANN 2025. Verleysen, M. (reds.). Ciaco - i6doc.com, blz. 419-424 6 blz.

Onderzoeksoutput › › peer review

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A.R. Engelsberger, MSc

Publicaties

FA(IR)2MA-GLVQ – A hidden-feature-bias mitigation approach for fairness in classification learning based on generalized matrix learning vector quantization

Prototype-Based Classifiers and Vector Quantization on a Quantum Computer: Implementing Integer Arithmetic Oracles for Nearest Prototype Search

Mitigating the Bias in Data for Fairness Using an Advanced Generalized Learning Vector Quantization Approach -- FA(IR)^2MA-GLVQ

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