Support Vector Machines (SVMs) have become one of the most popular supervised learning-machines in the statistical pattern recognition area. They are used for classification (i.e. SVM) and regression analysis (i.e. Support Vector Regression, SVR). However, when the number of samples available to model an SVM/SVR problem supersedes the computational resources (i.e. large-scale problems where the...

The time series prediction of avian influenza epidemics is a complex issue, because avian influenza has latent seasonality which is difficult to identify. Although researchers have applied a neural network (NN) model and the Box-Jenkins model for the seasonal epidemic series research area, the results are limited. In this study, we develop a new prediction seasonal auto-regressive-based support...

In this paper, a warning system is constructed using Gravitational Search Algorithm Support Vector Regression (GSA-SVR). The gravitational search algorithm (GSA) is used to optimize the regularization parameter of Support Vector Regression (SVR) and is compared to particle swarm optimization. First, the history data of each index are normalized to (0,1). Then, the weights of each index are dete...

As to classification problem, this paper puts forward the combinatorial optimization least squares support vector machine algorithm (COLS-SVM). Based on algorithmic analysis of COLS-SVM and improves on it, the improved COLS-SVM can be used on individual credit evaluation. As to regression problem, appropriate kernel function and parameters were selected based on the analysis of support vector r...

Linear regression (LR) and support vector regression (SVR) are widely used in data analysis. Geometrical correlation learning (GcLearn) was proposed recently to improve the predictive ability of LR and SVR through mining and using correlations between data of a variable (inner correlation). This paper theoretically analyzes prediction performance of the GcLearn method and proves that GcLearn LR...

support vector regression (svr) is a learning method based on the support vector machine (svm) that can be used for curve fitting and function estimation. in this paper, the ability of the nu-svr to predict the catalytic activity of the fischer-tropsch (ft) reaction is evaluated and the result is compared with two other prediction techniques including: multilayer perceptron (mlp) and subtractiv...

It’s known that incorporating prior knowledge into support vector regression (SVR) can help to improve the approximation performance. Most of researches are concerned with the incorporation of knowledge in form of numerical relationships. Little work, however, has been done to incorporate the prior knowledge on the structural relationships among the variables (referred as to Structural Prior Kn...

It’s known that incorporating prior knowledge into support vector regression (SVR) can help to improve the approximation performance. Most of researches are concerned with the incorporation of knowledge in the form of numerical relationships. Little work, however, has been done to incorporate the prior knowledge on the structural relationships among the variables (referred as to Structural Prio...

Hybridizing chaotic evolutionary algorithms with support vector regression (SVR) to improve forecasting accuracy is a hot topic in electricity load forecasting. Trapping at local optima and premature convergence are critical shortcomings of the tabu search (TS) algorithm. This paper investigates potential improvements of the TS algorithm by applying quantum computing mechanics to enhance the se...

Support vector regression (SVR) is used in this study to develop models to estimate apparent metabolizable energy (AME), AME corrected for nitrogen (AMEn), true metabolizable energy (TME), and TME corrected for nitrogen (TMEn) contents of corn fed to ducks based on its chemical composition. Performance of the SVR models was assessed by comparing their results with those of artificial neural net...