Regularization techniques and suboptimal solutions to optimization problems in learning from data.
Various regularization techniques are investigated in supervised learning from data. Theoretical features of the associated optimization problems are studied, and sparse suboptimal solutions are searched for. Rates of approximate optimization are estimated for sequences of suboptimal solutions formed by linear combinations of n-tuples of computational units, and statistical learning bounds are derived. As hypothesis sets, reproducing kernel Hilbert spaces and their subsets are considered.
Gnecco
Giorgio
G
Departments of Communications, Computer, and System Sciences and of Computer and Information Science, University of Genova, Genova, Italy. giorgio.gnecco@dist.unige.it
Sanguineti
Marcello
M
D000465:Algorithms; D001185:Artificial Intelligence; D016014:Linear Models; D013997:Time Factors