Feature selection is an important issue in the research areas of machine learning and data mining. It reduces the dimensionality of data and enhances the performance of data analysis and interpretability, such as clustering or classification algorithms. This paper proposes a feature selection method based on support vector machines and distance-based cumulative distribution functions. This meth...

Training support vector machines (SVMs) with nonlinear kernel functions on large-scale data are usually very timeconsuming. In contrast, there exist faster solvers to train the linear SVM. We propose a technique which sufficiently approximates the infinite-dimensional implicit feature mapping of the Gaussian kernel function by a low-dimensional feature mapping. By explicitly mapping data to the...

Support vector machines (SVM) have been promising methods for classification analysis due to their solid mathematical foundations. Clustering-based SVMs are used to solve large samples classification problems and reduce the computational cost. In this paper, we present a density clustering based SVM(DCB-SVM) method to predict polyadenylation signal (PAS) in human DNA and mRNA sequences. We decr...

This paper presents a novel hierarchical clustering method using support vector machines. A common approach for hierarchical clustering is to use distance for the task. However, different choices for computing inter-cluster distances often lead to fairly distinct clustering outcomes, causing interpretation difficulties in practice. In this paper, we propose to use a one-class support vector mac...

Support Vector Machines (SVMs) have become a popular classification tool. Because of their theoretical robustness they offer improvements in pattern classification applications. This paper describes an approach of producing a N-best list of hypotheses for the needs of phoneme recognition, using a Least Squares Support Vector Machine classifier (LS-SVM) and generate the corresponding N-best list...

Introduction: Multivariate pattern analysis (MVPA) of fMRI data has been growing in popularity due to its sensitivity to networks of brain activation [1]. Another benefit of MVPA is that it is performed in a predictive modeling framework which is natural for implementing brain state prediction and real-time fMRI applications such as brain computer interfaces [2]. Support vector machines (SVM) h...

in this study, a prediction model based on support vector machines (svm) improved by introducing a volume weighted penalty function to the model was introduced to increase the accuracy of forecasting short term trends on the stock market to develop the optimal trading strategy. along with vw-svm classifier, a hybrid feature selection method was used that consisted of f-score as the filter part ...

This paper aims to assess the application of Support Vector Machine (SVM) regression in order to analysis flexible pavements. To this end, 10000 Four-layer flexible pavement sections consisted of asphalt concrete layer, granular base layer, granular subbase layer, and subgrade soil were analyzed under the effect of standard axle loading using multi-layered elastic theory and pavement critical r...