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Gaussian kernel expansion with basis functions uniformly bounded in
Mauro Bisiacco, Gianluigi Pillonetto
Kernel expansions are a topic of considerable interest in machine learning, also because of their relation to the so-called feature maps introduced in machine learning. Properties…
Kernel-based function learning in dynamic and non stationary environments
Alberto Giaretta, Mauro Bisiacco, Gianluigi Pillonetto
One central theme in machine learning is function estimation from sparse and noisy data. An example is supervised learning where the elements of the training set are couples, each…
Mathematical foundations of stable RKHSs
Mauro Bisiacco, Gianluigi Pillonetto
Reproducing kernel Hilbert spaces (RKHSs) are key spaces for machine learning that are becoming popular also for linear system identification. In particular, the so-called stable R…
Kernel absolute summability is only sufficient for RKHS stability
Mauro Bisiacco, Gianluigi Pillonetto
Regularized approaches have been successfully applied to linear system identification in recent years. Many of them model unknown impulse responses exploiting the so called Reprodu…