Discriminative training of Gaussian Mixture Models (GMMs) for speech or speaker recognition purposes is usually based on the gradient descent method, in which the iteration step-size, ε, uses to be defined experimentally. ...

Two iterative schemes for the solution of an H-system with Dirichlet boundary data for a revolution surface are studied: a Newton imbedding type procedure, which yields the local quadratic convergence of the iteration and ...

We study ah iterative scheme for the computation of a zero of the sum of two nonlinear operators. The proposed method contains Newton scheme and the Modified Newton scheme as special cases and therefore provides a unified ...

We present new semilocal and local convergence results for the Newton–Kantorovich method. These new results extend the applicability of the Newton–Kantorovich method on approximate zeros by improving the convergence domain ...

Discriminative training of Gaussian Mixture Models (GMMs) for speech or speaker recognition purposes is usually based on the gradient descent method, in which the iteration step-size, ε, uses to be defined experimentally. ...

The main goal of this paper is to approximate some matrix functions by using a family of high-order Newton-type iterative methods. We analyse the semilocal convergence and the speed of convergence of these methods. Concerning ...

We study a one-step intermediate_method for the iterative computation of a zero of the sum of two nonlinear operators. The proposed method contains Newton scheme and the Modified Newton scheme as special cases and therefore ...