Add to ORKGThe paper presents a stochastic optimization algorithm for computing of least median of squares regression (LMS) introduced by (Rousseeuw and Leroy 1986). As the exact solution is hard to obtain a random approximation is proposed, which is much cheaper in time and easy to program. A MATLAB program is included
We study nonlinear least-squares problem that can be transformed to linear problem by change of vari...
We study nonlinear least-squares problem that can be transformed to linear problem by change of vari...
We study nonlinear least-squares problem that can be transformed to linear problem by change of vari...
We consider linear prediction problems in a stochastic environment. The least mean square (LMS) algo...
Adaptive Least Squares Matching (ALSM) is a powerful technique for precisely locating objects in dig...
Adaptive Least Squares Matching (ALSM) is a powerful technique for precisely locating objects in dig...
In this paper, we present a recursive algorithm for the solution of uncertain least-square problems ...
AbstractGiven n points {(xi, yi)} in the plane we study the problem of calculating the least median ...
Instead of minimizing the sum of all $n$ squared residuals as the classical least squares (LS) does,...
In this paper we present a very brief description of least mean square algorithm with applications i...
A general procedure is described for setting up monotonically convergent algorithms to solve some ge...
Stochastic optimization algorithms have been growing rapidly in popularity over the last decade or t...
A general procedure is described for setting up monotonically convergent algorithms to solve some ge...
A general procedure is described for setting up monotonically convergent algorithms to solve some ge...
For estimating the parameters of models for financial market data, the use of robust techniques is o...
We study nonlinear least-squares problem that can be transformed to linear problem by change of vari...
We study nonlinear least-squares problem that can be transformed to linear problem by change of vari...
We study nonlinear least-squares problem that can be transformed to linear problem by change of vari...
We consider linear prediction problems in a stochastic environment. The least mean square (LMS) algo...
Adaptive Least Squares Matching (ALSM) is a powerful technique for precisely locating objects in dig...
Adaptive Least Squares Matching (ALSM) is a powerful technique for precisely locating objects in dig...
In this paper, we present a recursive algorithm for the solution of uncertain least-square problems ...
AbstractGiven n points {(xi, yi)} in the plane we study the problem of calculating the least median ...
Instead of minimizing the sum of all $n$ squared residuals as the classical least squares (LS) does,...
In this paper we present a very brief description of least mean square algorithm with applications i...
A general procedure is described for setting up monotonically convergent algorithms to solve some ge...
Stochastic optimization algorithms have been growing rapidly in popularity over the last decade or t...
A general procedure is described for setting up monotonically convergent algorithms to solve some ge...
A general procedure is described for setting up monotonically convergent algorithms to solve some ge...
For estimating the parameters of models for financial market data, the use of robust techniques is o...
We study nonlinear least-squares problem that can be transformed to linear problem by change of vari...
We study nonlinear least-squares problem that can be transformed to linear problem by change of vari...
We study nonlinear least-squares problem that can be transformed to linear problem by change of vari...