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Sparse Orthogonal Regression Technique: A Spectral Framework for Equation Discovery, Approximation, and Integration
Sabin Roman, Ljupco Todorovski, Saso Dzeroski
We develop the Sparse Orthogonal Regression Technique (SORT), a sparse spectral framework for learning orthonormal-basis expansions from noisy and irregularly sampled data. SORT es…
Design Choices That Matter: A Functional ANOVA Analysis for Remote Sensing Multi-Label Classification
Maryam Gholami Shiri, Eva Tuba, Sašo Džeroski +2
Benchmarking deep learning (DL) models for multi-label classification (MLC) of remote sensing images (RSI) typically yields rankings that do not generalize beyond the evaluated dat…
Limits of spectral learning under noise
Sabin Roman, Ljupco Todorovski, Saso Dzeroski +2
Learning functional relationships from noisy data is a central problem in scientific inference. Spectral methods approximate unknown functions by expanding them in a basis and esti…
Uncertainty-Aware Extrapolation in Bayesian Oblique Trees
Viktor Andonovikj, Sašo Džeroski, Pavle Boškoski
Decision trees are widely used due to their interpretability and efficiency, but they struggle in regression tasks that require reliable extrapolation and well-calibrated uncertain…