Sentence examples for sequential optimisation from inspiring English sources

This paper further illustrates sequential optimisation of two design parameters (spacing and overlap) using numerical modelling.

Novel multi-objective optimisation methodologies, including a two-step sequential optimisation approach and multi-objective optimisation approaches using non-dominated sorting genetic algorithms (NSGAs) and MATLAB based linear programming integrated with genetic algorithms have been developed for the first time to engineer the cellular metabolic productivity and process performance simultaneously.

For the Arabidopsis circadian clock models, the parameter sets were given by sequential optimisation strategy against a semi-quantitative (or penalty) cost function [20] and a chi-square cost function [47].

The cement composition used to plot the eventual scaffolds was established after sequential optimisation steps.

Sequential Minimal Optimisation.

The dual QP problem can be rapidly solved by the Sequential Minimal Optimisation method, proposed by Platt [24].

SMO is Weka's implementation of the Support Vector Machine where the sequential minimal optimisation algorithm is used to train a support vector classifier.

The classifier used in the first order is Support Vector Machines (SVM) trained with Sequential Minimal Optimisation (SMO) [44], the complexity value set to and a linear kernel function.

In all experiments, we apply support vector machines (SVMs) with linear kernel function as implemented in WEKA [25] with sequential minimal optimisation (SMO) as training algorithm, due to their good baseline performance in many related speech emotion recognition studies (cf., e. g., [9] and [8]).

MFS combined with Naive Bayes and Sequential minimal optimisation (SMO for training of support vector machine) provided the best classification accuracies and TP (true positive) and F-measure resulted in a higher performance as compare to experimental setups based on state of the art feature selection techniques combined with machine learning classifiers.

An ensemble selection for feature-based classification of diabetic maculopathy images is suggested by Chowriappa et al. [44] based on extracting textural features and then classifying into the disease severity classes by using classifiers such as the hidden Naïve Bayes, Naïve Bayes, sequential minimal optimisation (SMO) and the tree-based J48 algorithm.