|Anewmulti-user receiver for synchronous CDMA systems with error control coding is proposed. The receiver achieves interference cancellation by projecting the undesired users onto the space spanned by the desired users' signal vectors. The detector calculates the Least Squares (LS) estimate of the interfering users data, that is used to yield an adjusted metric for Maximum-Likelihood Sequence E...

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) ...

The main goal of this paper is to study the asymptotic properties of least squares (LS) estimation for invertible and causal PARMAmodels with uncorrelated but dependent errors (weak PARMA). Four different LS estimators are considered: ordinary least squares (OLS), weighted least squares (WLS) for an arbitrary vector of weights, generalized least squares (GLS) in which the weights correspond to ...

We show how the quasi-Newton least squares method (QN-LS) relates to Krylov subspace methods in general and to GMRes in particular.

This paper contrasts three related regularization schemes for kernel machines using a least squares criterion, namely Tikhonov and Ivanov regularization and Morozov’s discrepancy principle. We derive the conditions for optimality in a least squares support vector machine context (LS-SVMs) where they differ in the role of the regularization parameter. In particular, the Ivanov and Morozov scheme...

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 ...

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...

The standard approaches to solving overdetermined linear systems Ax ≈ b construct minimal corrections to the vector b and/or the matrix A such that the corrected system is compatible. In ordinary least squares (LS) the correction is restricted to b, while in data least squares (DLS) it is restricted to A. In scaled total least squares (Scaled TLS) [15], corrections to both b and A are allowed, ...

In [16], authors described an algorithm based on Structured Total Least Norm (STLN) for constructing a Sylvester matrix of given lower rank and obtaining the nearest perturbed polynomials with exact GCD of given degree. For their algorithm, the overall computation time depends on solving a sequence least squares (LS) problems. In this paper, a fast implementation for solving these LS problems i...