second-order learning algorithm
Second-order Learning Algorithm with Squared Penalty Term
This paper compares three penalty terms with respect to the effi(cid:173) ciency of supervised learning, by using first- and second-order learn(cid:173) ing algorithms. Our experiments showed that for a reasonably ade(cid:173) quate penalty factor, the combination of the squared penalty term and the second-order learning algorithm drastically improves the convergence performance more than 20 times over the other com(cid:173) binations, at the same time bringing about a better generalization performance.
Second-order Learning Algorithm with Squared Penalty Term
This paper compares three penalty terms with respect to the efficiency of supervised learning, by using first-and second-order learning algorithms. Our experiments showed that for a reasonably adequate penalty factor, the combination of the squared penalty term and the second-order learning algorithm drastically improves the convergence performance more than 20 times over the other combinations, at the same time bringing about a better generalization performance.
Second-order Learning Algorithm with Squared Penalty Term
This paper compares three penalty terms with respect to the efficiency of supervised learning, by using first-and second-order learning algorithms. Our experiments showed that for a reasonably adequate penalty factor, the combination of the squared penalty term and the second-order learning algorithm drastically improves the convergence performance more than 20 times over the other combinations, at the same time bringing about a better generalization performance.
Second-order Learning Algorithm with Squared Penalty Term
This paper compares three penalty terms with respect to the efficiency ofsupervised learning, by using first-and second-order learning algorithms. Our experiments showed that for a reasonably adequate penaltyfactor, the combination of the squared penalty term and the second-order learning algorithm drastically improves the convergence performance more than 20 times over the other combinations, atthe same time bringing about a better generalization performance.