@article{eprints1708,
           pages = {53--79},
            year = {2009},
          volume = {6},
           title = {The weight-decay technique in learning from data: an optimization point of view},
          author = {Giorgio Gnecco and Marcello Sanguineti},
       publisher = {Springer},
         journal = {Computational Management Science},
          number = {1},
             url = {http://eprints.imtlucca.it/1708/},
        keywords = {Learning from data; Regularization; Weight decay; Suboptimal solutions; Rates of convergence},
        abstract = {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.}
}