A Global Linear Method for Camera Pose Registration

2013 ◽  
Author(s):  
Nianjuan Jiang ◽  
Zhaopeng Cui ◽  
Ping Tan
Keyword(s):  
Linear Method ◽  
Camera Pose

A linear method for determining the radius of the circular shape structures

2016 ◽  
Vol 917 (11) ◽  
pp. 11-15 ◽  
Author(s):  
G.A. Shekhovtsov ◽  
◽  
R.P. Shekhovtsova ◽  
O.V. Raskatkina ◽  
◽  
...  
Keyword(s):  
Linear Method ◽  
Circular Shape

scholarly journals Improved Predictive Ability of KPLS Regression with Memetic Algorithms

Mathematics ◽  
2021 ◽  
Vol 9 (5) ◽  
pp. 506
Author(s):  
Jorge Daniel Mello-Román ◽  
Adolfo Hernández ◽  
Julio César Mello-Román
Keyword(s):  
Kernel Function ◽  
Linear Method ◽  
Predictive Ability ◽  
Feature Space ◽  
Memetic Algorithms ◽  
Least Squares Regression ◽  
Optimal Parameters ◽  
Number Of Components ◽  
Kernel Partial Least Squares ◽  
Dependent Variables

Kernel partial least squares regression (KPLS) is a non-linear method for predicting one or more dependent variables from a set of predictors, which transforms the original datasets into a feature space where it is possible to generate a linear model and extract orthogonal factors also called components. A difficulty in implementing KPLS regression is determining the number of components and the kernel function parameters that maximize its performance. In this work, a method is proposed to improve the predictive ability of the KPLS regression by means of memetic algorithms. A metaheuristic tuning procedure is carried out to select the number of components and the kernel function parameters that maximize the cumulative predictive squared correlation coefficient, an overall indicator of the predictive ability of KPLS. The proposed methodology led to estimate optimal parameters of the KPLS regression for the improvement of its predictive ability.

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