01.01.2022 – 31.12.2022
We propose an approach to spectral regularization algorithms for kernel-based supervised learning with infinite-dimensional response variables. Recent research shows that this scenario enjoys widespread practical use. However, virtually no results exist due to the mathematical complexity compared to the finite-dimensional learning settings investigated so far.
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