The Least Mean Square (LMS) algorithm, introduced by Widrow and Hoff in 1959 [12] is an adaptive algorithm, which uses a gradient-based method of steepest decent [10]. LMS algorithm uses the estimates of the gradient vector from the available data. LMS incorporates an iterative procedure that makes successive corrections to the weight vector in the direction of the negative of the gradient vect...

Partial least square (PLS) analysis is the most favourite tool in chemometrics to develop calibration models. PLS technique allows us decipher even complex systems by analysing all variables instead of looking at them one a time. not only capture maximum variation associated with predictor (i.e. spectra) and predicted concentration) but also maximises correlation between them. The present artic...

The Least Mean Square (LMS) algorithm, introduced by Widrow and Hoff in 1959 [12] is an adaptive algorithm, which uses a gradient-based method of steepest decent [10]. LMS algorithm uses the estimates of the gradient vector from the available data. LMS incorporates an iterative procedure that makes successive corrections to the weight vector in the direction of the negative of the gradient vect...

Education is an important factor that can affect the development of a country and basic human need. To ensure continuous progress education, it necessary to pay attention results achievements education in Indonesia. In this research model, Partial Least Square Path Modeling (PLS PM) used explain relationship between outcomes quality dimensions provincial However, because there heterogeneity pop...

localizing the optic disc (od) in retinal fundus images is of critical importance, since it is very similar to hard exudates (found in pathology) in color and brightness. because of the importance of od localization in clinical applications, many techniques have been developed. in this paper, we present the results obtained from two methods, correlation and least square, to approximate the opti...