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...

The support vector machine (SVM) is a popular classifier in machine learning, but it is not robust to outliers. In this paper, based on the Correntropy induced loss function, we propose the rescaled hinge loss function which is a monotonic, bounded and nonconvex loss that is robust to outliers. We further show that the hinge loss is a special case of the proposed rescaled hinge loss. Then, we d...

This paper proposes the least-squares support vector machine (LS-SVM) as an intelligent method applied on absorption spectra for the simultaneous determination of paracetamol (PCT), caffeine (CAF) and ibuprofen (IB) in Novafen. The signal to noise ratio (S/N) increased. Also, In the LS - SVM model, Kernel parameter (σ2) and capacity factor (C) were optimized. Excellent prediction was shown usin...

Support vector machine (SVM) has been a promising method for classification and regression analysis because of its solid mathematical foundation which conveys several salient properties that other methods do not provide. However, despite the prominent properties of SVM, it is not as favored for large-scale data mining as for pattern recognition or machine learning because the training complexit...

To set the values of the hyperparameters of a support vector machine (SVM), the method of choice is cross-validation. Several upper bounds on the leave-one-out error of the pattern recognition SVM have been derived. One of the most popular is the radius–margin bound. It applies to the hard margin machine, and, by extension, to the 2-norm SVM. In this article, we introduce the first quadratic lo...

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...

Among Neural Network methods, the Support Vector Machine (SVM) solutions are attracting increasing attention, mostly because they automatically derive the “optimal” network structure, in respect to generalization error for a given problem. In practice it means, that a lot of decisions that had to be made during the design of a traditional NN (e.g. the number of neurons, the length and type of t...

this study aimed to examine the influence of pre-processing input variables by gamma test on performance of support vector machine in order to predict the suspended sediment amount of doiraj river, located in ilam province from 1994-2004. the flow discharge and rainfall were considered as the input variables and sediment discharge as the output model. also, the duration of the model training pe...

This paper examined bankruptcy predictive accuracy of five statistics models-discriminant analysis logistic regression, probit regression, neural networks, support vector machine (SVM), and genetic-based SVM (GA-SVM) that influenced by variable selection. Empirical results indicate that the SVM-based models are very promising models for predicting financial failure, in terms of both best predic...

The setting of parameters in the support vector machines (SVMs) is very important with regard to its accuracy and efficiency. In this paper, we employ the firefly algorithm to train all parameters of the SVM simultaneously, including the penalty parameter, smoothness parameter, and Lagrangian multiplier. The proposed method is called the firefly-based SVM (firefly-SVM). This tool is not conside...