3 papers
stat.ME2025
Uncertainty calibration for latent-variable regression models
Zina-Sabrina Duma, Otto Lamminpää, Jouni Susiluoto +6
Uncertainty quantification is essential for scientific analysis, as it allows for the evaluation and interpretation of variability and reliability in complex systems and datasets.…
cs.LG2024
Kernel-based retrieval models for hyperspectral image data optimized with Kernel Flows
Zina-Sabrina Duma, Tuomas Sihvonen, Jouni Susiluoto +3
Kernel-based statistical methods are efficient, but their performance depends heavily on the selection of kernel parameters. In literature, the optimization studies on kernel-based…
stat.ME2023
KF-PLS: Optimizing Kernel Partial Least-Squares (K-PLS) with Kernel Flows
Zina-Sabrina Duma, Jouni Susiluoto, Otto Lamminpää +3
Partial Least-Squares (PLS) Regression is a widely used tool in chemometrics for performing multivariate regression. PLS is a bi-linear method that has a limited capacity of modell…