A novel type of learning machine called support vector machine (SVM) has been receiving increasing interest in areas ranging from its original application in pattern recognition to other applications such as regression estimation due to its remarkable generalization performance. This paper deals with the application of SVM in financial time series forecasting. The feasibility of applying SVM in...

We investigate the problems of multiclass cancer classification with gene selection from gene expression data. Two different constructed multiclass classifiers with gene selection are proposed, which are fuzzy support vector machine (FSVM) with gene selection and binary classification tree based on SVM with gene selection. Using F test and recursive feature elimination based on SVM as gene sele...

Selective catalytic reduction (SCR) is one of the most effective technologies used for eliminating NOx from diesel engines. This paper presents a novel method based on a support vector machine (SVM) and particle swarm optimization (PSO) with grid search (GS) to diagnose the degree of aging of the V2O5/WO3–TiO2 catalyst in the SCR system. This study shows the aging effect on the performance of a...

This paper surveys the significance of sparsity for the Support Vector Machine (SVM) method. The SVM method is a machine learning technique with a wide range of applications, e.g. medical diagnosis, pattern recognition, and clustering. The method is fairly recent; Vapnik is credited with originating it in 1979. We present a general introduction to SVMs in the context of data classification and ...

This study presents a novel technique based on Support Vector Machine (SVM) for the classification of transient phenomena in power transformer. The SVM is a powerful method for statistical classification of data. The input data to this SVM for training comprises fault current and magnetizing inrush current. SVM classifier produces significant accuracy for classification of transient phenomena i...

The support vector machine (SVM) is one of the most widely used approaches for data classification and regression. SVM achieves the largest distance between the positive and negative support vectors, which neglects the remote instances away from the SVM interface. In order to avoid a position change of the SVM interface as the result of an error system outlier, C-SVM was implemented to decrease...

Loan default evaluation and discrimination is a complicated issue because of its nonlinearity and uncertainty. Least square support vector machine (LS-SVM) has been successfully employed to solve regression and time series problem. This paper proposes a novel PSO-LS-SVM model based on the improved PSO algorithm to optimize parameters of LS-SVM, which is a new improved form by synthesizing the e...

This paper focuses on the feature selection in classification via a new version of support vector machine (SVM) named p-norm support vector machine (0 < p < 1). Different from the 2-norm in the standard linear SVM, the p-norm of the normal vector of the decision plane is used which leads to more sparse solution. By using the successive linear algorithm, we can get an approximate local optimal s...