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 ...

Least squares (LS) estimation has been extensively used for parameter identification and model-based diagnosis. However, if ill-conditioning is present, the LS estimation approach tends to generate imprecise results and thus impacts the diagnostic performance. In this paper, an adjusted least squares approach is proposed to deal with the illconditioning problem in the diagnosis of compliant she...

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 proposes the least-squares support vector machine (LS-SVM) as an intelligent method applied on absorption spectra for the simultaneous determination of paracetamol (PCT), caffeine (CAF) and ibuprofen (IB) in Novafen. The signal to noise ratio (S/N) increased. Also, In the LS - SVM model, Kernel parameter (σ2) and capacity factor (C) were optimized. Excellent prediction was shown usin...

Localization of a moving target in a dual-frequency radars system has now gained considerable attention. The noncoherent localization approach based on a least squares (LS) estimator has been addressed in the literature. Compared with the LS method, a novel localization method based on a two-step weighted least squares estimator is proposed to increase positioning accuracy for a multi-station d...

This paper deals with the problem of automatic rule generation for fuzzy systems design. The proposed approach is based on hybrid artificial bee colony (ABC) optimization and weighted least squares (LS) method and aims to find the structure and parameters of fuzzy systems simultaneously. More precisely, two ABC based fuzzy modeling strategies are presented and compared. The first strategy uses ...

The following is a brief review of least squares optimization and constrained optimization techniques. I assume the reader is familiar with basic linear algebra, including the Singular Value decomposition (as reviewed in my handout Geometric Review of Linear Algebra). Least squares (LS) problems are those in which the objective function may be expressed as a sum of squares. Such problems have a...

The aim of this study is to develop the Bayesian Least Squares Support Vector Machine (LS-SVM) classifiers, for preoperatively predicting the malignancy of ovarian tumors. We describe how to perform parameter estimation, input variable selection for LS-SVM within the evidence framework. The issue of computing the posterior class probability for risk minimization decision making is addressed. Th...

This paper investigates the use of a total least squares approach in a generalization of the iterative closest point (ICP) algorithm for shape registration. A new Generalized Total Least Squares (GTLS) formulation of the minimization process is presented opposed to the traditional Least Squares (LS) technique. Accounting for uncertainty both in the target and in the source models will lead to a...

Deep reinforcement learning (DRL) methods such as the Deep Q-Network (DQN) have achieved state-of-the-art results in a variety of challenging, high-dimensional domains. This success is mainly attributed to the power of deep neural networks to learn rich domain representations for approximating the value function or policy. Batch reinforcement learning methods with linear representations, on the...