On a linear method of approximation for functions with bounded derivative

1977 ◽  
Vol 28 (4) ◽  
pp. 431-432
Author(s):  
R. A. Raitsin
Keyword(s):  
Linear Method ◽  
Bounded Derivative

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.

scholarly journals Moduli Space of Brody Curves, Energy and Mean Dimension

2008 ◽  
Vol 192 ◽  
pp. 27-58 ◽  
Author(s):  
Masaki Tsukamoto
Keyword(s):  
Complex Plane ◽  
Moduli Space ◽  
Hermitian Manifold ◽  
Value Distribution ◽  
Holomorphic Map ◽  
Infinite Dimensional ◽  
Mean Dimension ◽  
The Mean ◽  
Moduli Theory ◽  
Bounded Derivative

AbstractA Brody curve is a holomorphic map from the complex plane ℂ to a Hermitian manifold with bounded derivative. In this paper we study the value distribution of Brody curves from the viewpoint of moduli theory. The moduli space of Brody curves becomes infinite dimensional in general, and we study its “mean dimension”. We introduce the notion of “mean energy” and show that this can be used to estimate the mean dimension.

Prediction of Construction Schedule Based on BP Neural Network

2013 ◽  
Vol 405-408 ◽  
pp. 3423-3428
Author(s):  
Zhao Lin Li ◽  
Guo Zhi Zhang
Keyword(s):  
Neural Network ◽  
Project Management ◽  
Bp Neural Network ◽  
Linear Method ◽  
Schedule Control ◽  
Construction Schedule

Schedule control is the major issue in project management, and to predict the construction schedule effectively is important practically. The article mainly predicts the schedule of a project based on BP neural network. The result shows that the predicted value is more accurate than the value calculated by linear method.

Multi-focal nematode image stack classification using a projection-based multi-linear method

2017 ◽  
Vol 29 (1) ◽  
pp. 135-144
Author(s):  
Min Liu ◽  
Xueping Wang ◽  
Keran Liu ◽  
Xiaoyan Liu
Keyword(s):  
Linear Method ◽  
Image Stack

A linear method for curve comparison in gait data

2009 ◽  
Vol 30 ◽  
pp. S53-S54 ◽  
Author(s):  
M. Iosa ◽  
A. Cereatti ◽  
A. Cappozzo
Keyword(s):  
Linear Method
Sign in / Sign up

Export Citation Format

Share Document