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