This paper proposes a novel application of Grammatical Bee Colony for classification of medical data. Grammatical Bee Colony is a Swarm Programming algorithm generally used for automatic computer program generation in any arbitrary language. In this paper, Grammatical Bee Colony based classifier is designed and applied in medical data mining. The proposed method is applied on ten medical data s...

This paper considers REML (residual or restricted maximum likelihood) estimation for heteroscedastic linear models. An explicit algorithm is given for REML-scoring which yields the REML estimates together with their standard errors and likelihood values. The algorithm includes a Levenberg-Marquardt restricted step modification which ensures that the REML-likelihood increases at each iteration. ...

This paper presents a tensor approximation algorithm, based on the Levenberg–Marquardt method for nonlinear least square problem, to decompose large-scale tensors into sum of products vector groups given scale, or obtain low-rank without losing too much accuracy. An Armijo-like rule inexact line search is also introduced this algorithm. The result decomposition adjustable, which implies that ca...

This paper proposes a novel application of Grammatical Bee Colony for classification of medical data. Grammatical Bee Colony is a Swarm Programming algorithm generally used for automatic computer program generation in any arbitrary language. In this paper, Grammatical Bee Colony based classifier is designed and applied in medical data mining. The proposed method is applied on ten medical data s...

This paper proposes a novel application of Grammatical Bee Colony for classification of medical data. Grammatical Bee Colony is a Swarm Programming algorithm generally used for automatic computer program generation in any arbitrary language. In this paper, Grammatical Bee Colony based classifier is designed and applied in medical data mining. The proposed method is applied on ten medical data s...

In this paper, we present a barrier method for solving nonlinear programming problems. It employs a Levenberg-Marquardt perturbation to the Karush-Kuhn-Tucker (KKT) matrix to handle indefinite Hessians and a line search to obtain sufficient descent at each iteration. We show that the Levenberg-Marquardt perturbation is equivalent to replacing the Newton step by a cubic regularization step with ...

In the present work, an artificial neural network (ANN) model was used to study the quantitative structure retention relationship (QSRR) of retention index (RI) of some volatile compounds in natural cocoa and conched chocolate powder. Molecular structural descriptors are selected using genetic algorithm to construct the nonlinear QSRR models, kernel partial least squares PLS (KPLS) and Levenber...

Unconstrained optimization problems are closely related to systems of ordinary differential equations (ODEs) with gradient structure. In this work, we prove results that apply to both areas. We analyze the convergence properties of a trust region, or Levenberg–Marquardt, algorithm for optimization. The algorithm may also be regarded as a linearized implicit Euler method with adaptive timestep f...

One of the most important processes in the early stages of construction projects is to estimate the cost involved. This process involves a wide range of uncertainties, which make it a challenging task. Because of unknown issues, using the experience of the experts or looking for similar cases are the conventional methods to deal with cost estimation. The current study presents data-driven metho...