In this paper, we propose some improvements for the implementations of least squares support vector machine classifiers (LS-SVM). An improved conjugate gradient scheme is proposed for solving the optimization problems in LS-SVM, and an improved SMO algorithm is put forward for the general unconstrained quadratic programming problems which is the case of LS-SVM without the bias term. Numerical e...

Extreme Support Vector Machine (ESVM), a variant of ELM, is a nonlinear SVM algorithm based on regularized least squares optimization. In this chapter, a regression algorithm, Extreme Support Vector Regression (ESVR), is proposed based on ESVM. Experiments show that, ESVR has a better generalization ability than the traditional ELM.Furthermore, ESVMcan reach comparable accuracy as SVR and LS-SV...

Based on fuzzy one-class support vector machine (SVM) and least squares (LS) oneclass SVM, we propose an LS fuzzy one-class SVM to deal with the class imbalanced problem. The LS fuzzy one-class SVM applies a fuzzy membership to each sample and attempts to solve the modified primal problem. Hence, we just need to solve a system of linear equations as opposed solving the quadratic programming pro...

A simple and rapid method for the determination of Ba isotope abundances in water samples by inductively coupled plasma-optical emission spectrometry (ICP-OES) coupled with least-squares support vector machine regression (LS-SVM) is reported. By evaluation of emission lines of barium, it was found that the emission line at 493.408 nm provides the best results for the determination of Ba abundan...

a quantitative structure-activity relationship (qsar) study was conducted for the prediction of inhibitory activity of 1-phenyl[2h]-tetrahydro-triazine-3-one analogues as inhibitors of 5-lipoxygenase. the inhibitory activities of the 1-phenyl[2h]-tetrahydro-triazine-3-one analogues modeled as a function of molecular structures using chemometrics methods such as multiple linear regression (mlr) ...

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...

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...

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...

This paper deals with the problem of multi-class classification in machine learning. Various techniques have been successfully proposed to solve such problems, with a computation cost often much higher than techniques dedicated to binary classification. To address this problem, we propose a novel formulation for designing multi-class classifiers, with essentially the same computational complexi...

In comparison to the original SVM, which involves a quadratic programming task; LS–SVM simplifies the required computation, but unfortunately the sparseness of standard SVM is lost. Another problem is that LS-SVM is only optimal if the training samples are corrupted by Gaussian noise. In Least Squares SVM (LS–SVM), the nonlinear solution is obtained, by first mapping the input vector to a high ...