Among neural models the Support Vector Machine (SVM) solutions are attracting increasing attention, mostly because they eliminate certain crucial questions involved by neural network construction. The main drawback of standard SVM is its high computational complexity, therefore recently a new technique, the Least Squares SVM (LS–SVM) has been introduced. In this paper we present an extended vie...

Recently, a new type of neural networks called Least Squares Support Vector Machines (LS-SVMs) has been receiving increasing attention in nonlinear system identification and control due to its generalization performance. This paper develops a stable adaptive control scheme using the LS-SVM network. The developed control scheme includes two parts: the identification part that uses a modified str...

In this paper we study nonlinear ARX models in relation to a class of kernel based models which make use of kernel induced feature spaces, a methodology which is common in the area of support vector machines (SVMs). Methods are proposed for extending the use of least squares support vector machine (LS-SVM) models towards a robust setting. In order to understand the robustness of these estimator...

the combinations of inductively coupled plasma-optical emission spectrometry (icp-oes) and three classification algorithms, i.e., partial least squares discriminant analysis (pls-da), least squares support vector machine (ls-svm) and soft independent modeling of class analogies (simca), for discriminating different brands of iranian bottled mineral waters, were explored. icp-oes was used for th...

This paper presents the Optimally-Pruned Extreme Learning Machine (OP-ELM) toolbox. This novel, fast and accurate methodology is applied to several regression and classification problems. The results are compared with widely known Multilayer Perceptron (MLP) and Least-Squares Support Vector Machine (LS-SVM) methods. As the experiments (regression and classification) demonstrate, the OP-ELM meth...

This paper is concerned with kernel-based techniques for automated categorization of laryngeal colour image sequences obtained by video laryngostroboscopy. Features used to characterize a laryngeal image are given by the kernel principal components computed using the N -vector of the 3-D colour histogram. The least squares support vector machine (LS-SVM) is designed for categorizing an image se...

Stock market prediction is the act of trying to determine the future value of a company stock or other financial instrument traded on a financial exchange. The successful prediction of a stock's future price will maximize investor’s gains. This paper proposes a machine learning model to predict stock market price. The proposed algorithm integrates Particle swarm optimization (PSO) and least squ...

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...

A common task in signal processing is to approximate adequately a signal. It is crucial to understand various methods to this task. In this paper we survey three different approximation methods: least squares support vector machine (LS-SVM), multiresolution signal approximation (MSA), and least squares approximation (LSA) to highlight their mathematical relationship. Based on the theoretical an...

IPv6 has enough IP addresses to solve the problem of lack of IP address space. However, there are many security problems to be concerned. The detection ability of current intrusion detection system is poor when given less priori knowledge. In this paper, we analyze the Least Squares Support Vector Machine (LS-SVM) algorithm and the working process of snort intrusion detection system. And then w...