We propose a family of novel cost-sensitive boosting methods for multi-class classification by applying the theory of gradient boosting to p-norm based cost functionals. We establ...
In this paper, we consider the problem of minimizing a non-smooth convex problem using first-order methods. The number of iterations required to guarantee a certain accuracy for ...
In this paper we present a method of parameter optimization, relative trust-region learning, where the trust-region method and the relative optimization [21] are jointly exploited...
In this paper, we extend the adjoint error correction of Pierce and Giles [SIAM Review, 42 (2000), pp. 247-264] for obtaining superconvergent approximations of functionals to Gale...
A simultaneous perturbation stochastic approximation (SPSA) method has been developed in this paper, using the operators of perturbation with the Lipschitz density function. This ...