TY  - JOUR
PB  - MIT Press
VL  - 22
EP  - 829
SN  - 0899-7667
A1  - Gnecco, Giorgio
A1  - Sanguineti, Marcello
SP  - 793
Y1  - 2010///
TI  - Regularization Techniques and Suboptimal Solutions to Optimization Problems in Learning from Data
ID  - eprints1713
AV  - none
N2  - 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.
JF  - Neural Computation
IS  - 3
UR  - http://dx.doi.org/10.1162/neco.2009.05-08-786
ER  -