Adaptive equalizers are used in digital communication system receivers to mitigate the effects of non-ideal channel characteristics and to obtain reliable data transmission. In this paper, we adopt least squares support vector machines (LS-SVM) for adaptive communication channel equalization. The LS-SVM involves equality instead of inequality constraints and works with a least squares cost func...

This paper introduces a software tool SYM-LS-SVM-SOLVER written in Maple to derive the dual system and the dual model representation of LS-SVM based models, symbolically. SYM-LS-SVM-SOLVER constructs the Lagrangian from the given objective function and list of constraints. Afterwards it obtains the KKT (Karush-Kuhn-Tucker) optimality conditions and finally formulates a linear system in terms of...

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) ...

In this article we introduce a software framework for embedded online data fusion on different levels of data abstraction. We present our data oriented fusion model and introduce the main functional units. The paper is focused to the decision modeling process. In our approach we use Support Vector Machines (SVM) as well as Least Squares SVM (LS-SVM) for decision modeling. Due to the computation...

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...

This paper proposes the use of least-squares support vector machines (LS-SVMs) as a relatively new nonlinear multivariate calibration method, capable of dealing with ill-posed problems. LS-SVMs are an extension of "traditional" SVMs that have been introduced recently in the field of chemistry and chemometrics. The advantages of SVM-based methods over many other methods are that these lead to gl...

Weighted least squares support vector machine (WLSSVM) is a robust version of least squares support vector machine (LS-SVM). It adds weights on error variables to eliminate the influence of outliers. But the weights, which largely depend on the original regression errors from unweighted LS-SVM, might be unreliable for correcting the biased estimation of LS-SVM, especially for the training data ...

We propose a snowing model to iteratively smoothe the various image noises while preserving the important image structures such as edges and lines. Considering the gray image as a digital terrain model, we develop an adaptive weighted least squares support vector machine (LS-SVM) to iteratively estimate the optimal gray surface underlying the noisy image. The LS-SVM works on Gaussian noise whil...

In this paper, we propose to use a quantitative approach based on LS-SVM to perform estimation of the impact of lossy compression on remote sensing image compression. Kernel function selection and the model parameters computation are studied for remote sensing image classification when LS-SVM analysis model is establish. The experiments show that our LS-SVM model achieves a good performance in ...