Tool fault diagnosis in numerical control (NC) machines plays a significant role in ensuring manufacturing quality. However, current methods of tool fault diagnosis lack accuracy. Therefore, in the present paper, a fault diagnosis method was proposed based on stationary subspace analysis (SSA) and least squares support vector machine (LS-SVM) using only a single sensor. First, SSA was used to e...

This paper proposes a novel modelling and optimization approach for steady state and transient performance tune-up of an engine at idle speed. In terms of modelling, Latin hypercube sampling and multiple-input and multiple-output (MIMO) least-squares support vector machines (LS-SVMs) are proposed to build an engine idle-speed model based on experimental sample data. Then, a genetic algorithm (G...

The solution of least squares support vector machines LS-SVMs is characterized by a specific linear system, that is, a saddle point system. Approaches for its numerical solutions such as conjugate methods Sykens and Vandewalle 1999 and null space methods Chu et al. 2005 have been proposed. To speed up the solution of LS-SVM, this paper employs the minimal residual MINRES method to solve the abo...

We study the relationship between Support Vector Machines (SVM) and Least Squares SVM (LS-SVM). Our main result shows that under mild conditions, LS-SVM for binaryclass classifications is equivalent to the hard margin SVM based on the well-known Mahalanobis distance measure. We further study the asymptotics of the hard margin SVM when the data dimensionality tends to infinity with a fixed sampl...

The key method in this thesis is least squares support vector machines (LSSVM), a class of kernel based learning methods that fits within the penalized modelling paradigm. Primary goals of the LS-SVM models are regression and classification. Although local methods (kernel methods) focus directly on estimating the function at a point, they face problems in high dimensions. Therefore, one can gua...

Clusters of Microcalcifications (MCCs) are most frequent symptoms of Ductal Carcinoma in Situ (DCIS) recognized by mammography. Least-Square Support Vector Machine (LS-SVM) is a variant of the standard SVM. In the paper, LS-SVM is proposed as a classifier for classifying MCCs as benign or malignant based on relevant extracted features from enhanced mammogram. To establish the credibility of LS-...

Least squares support vector machines (LS-SVM) is an SVM version which involves equality instead of inequality constraints and works with a least squares cost function. In this way, the solution follows from a linear Karush–Kuhn–Tucker system instead of a quadratic programming problem. However, sparseness is lost in the LS-SVM case and the estimation of the support values is only optimal in the...

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...