Least squares twin support vector machine (LS-TSVM) aims at resolving a pair of smaller-sized quadratic programming problems (QPPs) instead of a single large one as in the conventional least squares support vector machine (LS-SVM), which makes the learning speed of LS-TSVM faster than that of LS-SVM. However, same penalties are given to the negative samples when constructing the hyper-plane for...

Among all statistics, government statistics plays an extremely important role in socioeconomic activities, while the continuous changes of external factors bring new challenges to and have a great impact on quality statistics. With rapid development economy maturity market system, demand for from walks life has become more vigorous. In order effectively improve accuracy macroeconomic outflow fo...

There has been a growing research interest in brain tumor classification based on proton magnetic resonance spectroscopy (1H MRS) signals. Four research centers within the EU funded INTERPRET project have acquired a significant number of long echo 1H MRS signals for brain tumor classification. In this paper, we present an objective comparison of several classification techniques applied to the ...

The Bayesian evidence framework has been successfully applied to the design of multilayer perceptrons (MLPs) in the work of MacKay. Nevertheless, the training of MLPs suffers from drawbacks like the nonconvex optimization problem and the choice of the number of hidden units. In support vector machines (SVMs) for classification, as introduced by Vapnik, a nonlinear decision boundary is obtained ...

For an underwater Strapdown Inertial Navigation System/Doppler velocity log (SINS/DVL) integrated navigation system, the short-term failure of DVL may lead to loss reliable external information from DVL, which will cause SINS errors accumulate. To circumvent this problem, paper proposes a predictor based on fuzzy multi-output least squares support vector machine (FMLS-SVM) predict measurements ...

Support Vector Machines is a powerful methodology for solving problems in nonlinear classification, function estimation and density estimation which has also led recently to many new developments in kernel based learning in general. In these methods one solves convex optimization problems, typically quadratic programs. We focus on Least Squares Support Vector Machines which are reformulations t...

Abstract This study employed soft computing techniques, namely, support vector machine (SVM) and Gaussian process regression (GPR) to predict the properties of a scour hole [depth (ds) length (Ls)] in diversion channel flow system. The considered different geometries channels (angles bed widths) hydraulic conditions. Four kernel function models for each technique (polynomial function, normalize...

Abstract- With the advancement and development of computer network technologies, the way for intruders has become smoother; therefore, to detect threats and attacks, the importance of intrusion detection systems (IDS) as one of the key elements of security is increasing. One of the challenges of intrusion detection systems is managing of the large amount of network traffic features. Removing un...