TY  - JOUR
SP  - 53
A1  - Gnecco, Giorgio
A1  - Sanguineti, Marcello
PB  - Springer
EP  - 79
VL  - 6
IS  - 1
UR  - http://dx.doi.org/10.1007/s10287-008-0072-5
KW  - Learning from data; Regularization; Weight decay; Suboptimal solutions; Rates of convergence
AV  - none
TI  - The weight-decay technique in learning from data: an optimization point of view
ID  - eprints1708
Y1  - 2009///
SN  - 1619-697X
N2  - The technique known as ?weight decay? in the literature about learning from data is investigated using tools from regularization theory. Weight-decay regularization is compared with Tikhonov?s regularization of the learning problem and with a mixed regularized learning technique. The accuracies of suboptimal solutions to weight-decay learning are estimated for connectionistic models with a-priori fixed numbers of computational units.
JF  - Computational Management Science
ER  -