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https://www.worldscientific.com/worldscibooks/10.1142/5089
Nov 01, 2002 · This book focuses on Least Squares Support Vector Machines (LS-SVMs) which are reformulations to standard SVMs. LS-SVMs are closely related to regularization networks and Gaussian processes but additionally emphasize and exploit primal-dual …
https://www.researchgate.net/publication/220578095_Least_Squares_Support_Vector_Machine_Classifiers
In least squares support vector machines (LS-SVMs) for function estimation Vapnik's ε-insensitive loss function has been replaced by a cost function which corresponds to a form of ridge regression.
http://homepages.rpi.edu/~embrem/dm/Suykens_tutorialucl.pdf
Least Squares Support Vector Machines Johan Suykens K.U. Leuven, ESAT-SCD-SISTA Kasteelpark Arenberg 10 B-3001 Leuven (Heverlee), Belgium Tel: 32/16/32 18 02 - Fax: 32/16/32 19 70
https://arxiv.org/pdf/1505.05451v1
Fuzzy Least Squares Twin Support Vector Machines Javad Salimi Sartakhtia,, Nasser Ghadiri a, Homayun Afrabandpey , Narges Yousefnezhadb aDepartment of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan, 84156-83111, IRAN bDepartment of Computer Engineering, Sharif University of Technology, Tehran, 11365-11155, IRAN Abstract Least Squares Twin Support …Cited by: 7
https://www.sciencedirect.com/science/article/pii/S0952197619301575
Least Squares Twin Support Vector Machine (LST-SVM) has been shown to be an efficient and fast algorithm for binary classification. In many real-world applications, samples may not deterministically be assigned to a single class; they come naturally with their associated uncertainties Also, samples may not be equally important and their importance degrees affect the classification.Author: Javad Salimi Sartakhti, Homayun Afrabandpey, Nasser Ghadiri
https://www.sciencedirect.com/science/article/pii/S0925231201006440
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.Cited by: 1444
https://link.springer.com/article/10.1023%2FA%3A1018628609742
Abstract. In this letter we discuss a least squares version for support vector machine (SVM) classifiers. Due to equality type constraints in the formulation, the solution follows from solving a set of linear equations, instead of quadratic programming for classical SVM's.Cited by: 9218
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