As a multi-classification problem, classification of moving vehicles has been studied by different statistical methods. These practical applications have various requirements, efficiencies, and performance, such as the size of training sample sets, convergence rate, and inseparable or ambiguous classification issues. With a reduction in its training time, the one-to-many support vector machine ...

Over the past two decades, support vector machine (SVM) has become a popular supervised learning model, and plenty of distinct algorithms are designed separately based on different KKT conditions SVM model for classification/regression with losses, including convex loss or nonconvex loss. In this paper, we propose an algorithm that can train models in \emph{unified} scheme. First, introduce def...

This paper presents a new method for real and reactive power tracing in a deregulated power system by introducing the hybrid artificial bee colony (ABC) algorithm and least squares support vector machine (LS-SVM), namely as ABC-SVM. The idea is to use ABC algorithm to obtain the optimal values of regularization parameter, c and Kernel RBF parameter, r, which are embedded in LS-SVM toolbox and a...

Robust cancer molecular pattern identification from microarray data not only plays an essential role in modern clinic oncology, but also presents a challenge for statistical learning. Although principal component analysis (PCA) is a widely used feature selection algorithm in microarray analysis, its holistic mechanism prevents it from capturing the latent local data structure in the following c...

The paper presents a novel learning algorithm for the class of L2 Support Vector Machines classifiers dubbed Direct L2 SVM. The proposed algorithm avoids solving the quadratic programming problem and yet, it produces both the same exact results as the classic quadratic programming based solution in a significantly shorter CPU time. The connections between various L2 SVM algorithms will be highl...

Introduction: Raman spectroscopy, that is a spectroscopic technique based on inelastic scattering of monochromatic light, can provide valuable information about molecular vibrations, so using this technique we can study molecular changes in a sample. Material and Methods: In this research, 153 Raman spectra obtained from normal and dried skin samples. Baseline and electrical noise were eliminat...

The Support Vector Machines (SVM) is a classification algorithm with many diverse applications. The SVM has many parameters associated with it which influences the performance of the SVM classifier. In this paper, we employ Genetic Algorithm based approach to find and select an appropriate kernel function and its parameters. This proposed technique combines predictive accuracy and complexity of...

Nonconvex variants of support vector machines (SVMs) have been developed for various purposes. For example, robust SVMs attain robustness to outliers by using a nonconvex loss function, while extended [Formula: see text]-SVM (E[Formula: see text]-SVM) extends the range of the hyperparameter by introducing a nonconvex constraint. Here, we consider an extended robust support vector machine (ER-SV...