Estimation of functions from sparse and noisy data is a central theme in machine learning. In
the last few years, many algorithms have been developed that exploit Tikhonov …

Many approaches to image restoration are aimed at removing either Gaussian or uniform
impulsive noise. This is because both types of degradation processes are distinct in nature …

Objective: Type-1 diabetes (T1D) is a disease characterized by impaired blood glucose (BG)
regulation, forcing patients to multiple daily therapeutic actions, including insulin …

In this paper we consider the problem of predicting future values of subcutaneous glucose
(glucose concentration in the interstitial fluid) in Type-1 diabetes patients, exploiting …

In this paper, the framework of kernel machines with two layers is introduced, generalizing
classical kernel methods. The new learning methodology provide a formal connection …

In this paper, we analyze the convergence of two general classes of optimization algorithms
for regularized kernel methods with convex loss function and quadratic norm regularization …

Abstract Type 1 diabetes (T1D) is a metabolic disease which impairs insulin production, and
it results in altered glucose homeostasis. As a consequence, subjects must frequently self …

First of all, I wish to thank my supervisor, Giuseppe De Nicolao, for his excellent guidance
and valuable advice that helped me to make the first steps into the world of research …

We analyze the problem of data sets reduction for support vector classification. The work is
also motivated by distributed problems, where sensors collect binary measurements at …

This paper considers the error bounds for the coef cient regularized regression schemes
associated with Lipschitz loss. Our main goal is to study the convergence rates for this …