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https://www.sciencedirect.com/science/article/pii/S0019057819303829
Recently, a robust least squares support vector machine (R-LSSVM) was proposed, but its computational complexity is very high compared with the traditional least squares support vector machine (LSSVM). To reduce R-LSSVM’s computational complexity, an improved version, i.e., extended LSSVM (E-LSSVM), is developed in this paper.Author: Yong-Ping Zhao, Jian-Jun Wang, Xiao-Ya Li, Guo-Jin Peng, Zhe Yang
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In least squares support vector machines (LS-SVMs) for function estimation Vapnik's ε-insensitive loss function has been replaced by a cost function which corresponds to a form of ridge regression.
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
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://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.researchgate.net/publication/308632720_Least_Squares_Support_Vector_Machines
A modified support vector machine (SVM) technique, namely, the least squares support vector machine (LS-SVM), is used to develop and train the thermal sensation classifier [22,23].
https://cs.adelaide.edu.au/~chhshen/teaching/ML_SVR.pdf
squared errors Least square estimation •The solution can be found by solving (By taking the derivative of the above objective function w.r.t. ) •In MATLAB, the back-slash operator computes ... Support Vector Regression •Find a function, f(x), with at most -deviation
https://lirias2repo.kuleuven.be/bitstream/handle/123456789/218716/Suykens_NeurProcLett.pdf;sequence=2
are zero (nonzero values correspond to support vectors), in least squares SVM’s the support values are proportional to the errors. This paper is organized as follows. In Section 2 we review some basic work about support vector machine classifiers. In Section 3 we discuss the least squares support vector machine classifiers.
https://www.cs.mcgill.ca/~rshah3/least_squares_svm.pdf
number of non-zero i’s; the corresponding ˚(xi) are called support vectors and hence S is the number of support vectors. 1.2 The Least Squares Approach There are many aspects of Vapnik’s SVM that make it attractive; for instance the sparse-ness of the solution vector , the implicit rede ning of the optimisation in the input space etc.
https://www.researchgate.net/publication/308632720_Least_Squares_Support_Vector_Machines
... Among the support vector machines (SVM) [1,2], the least squares support vector machine (LSSVM) is considered as a variation of the standard support vector machine (SVM) developed by Suykens et al. [3]. The concept of LSSVM has been successfully applied in many literatures to achieve good results. ...
https://link.springer.com/article/10.1023%2FB%3AMACH.0000008082.80494.e0
In Support Vector Machines (SVMs), the solution of the classification problem is characterized by a (convex) quadratic programming (QP) problem. In a modified version of SVMs, called Least Squares SVM classifiers (LS-SVMs), a least squares cost function is proposed so as to obtain a linear set of equations in the dual space. While the SVM classifier has a large margin interpretation, the LS ...
https://dsp.stackexchange.com/questions/48812/python-least-squares-support-vector-machine-ls-svm
I'm looking for a Python package for a LS-SVM or a way to tune a normal SVM from scikit-learn to a Least-Squares Support Vector Machine for a classification problem. The goal of a SVM is to maximize the margin while softly penalizing points that lie on the wrong side of the margin boundary.
https://www.academia.edu/21678942/Multiclass_least_squares_support_vector_machines
Multiclass least squares support vector machines
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 Vector …
https://pubs.acs.org/doi/abs/10.1021/ac035522m
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 ...
https://www.mathworks.com/help/optim/ug/least-squares-model-fitting-algorithms.html
Least-Squares (Model Fitting) Algorithms Least Squares Definition. Least squares, in general, is the problem of finding a vector x that is a local minimizer to a function that is a sum of squares, possibly subject to some constraints: ... Web browsers do not support MATLAB commands.
https://en.wikipedia.org/wiki/Support-vector_machine
Analogously, the model produced by SVR depends only on a subset of the training data, because the cost function for building the model ignores any training data close to the model prediction. Another SVM version known as least-squares support-vector machine (LS-SVM) has been proposed by …
https://www.kdnuggets.com/2017/03/building-regression-models-support-vector-regression.html
The article studies the advantage of Support Vector Regression (SVR) over Simple Linear Regression (SLR) models for predicting real values, using the same basic idea as Support Vector Machines (SVM) use for classification.
https://www.ijser.org/paper/Brain-MR-Image-Classification-Using-Least-Squares-Support-Vector-Machine.html
The results of Least Squares Support Vector Machine are analysed to detect the tumour type. 3.1 Medical Image / MR Image ( Input Image Data) Magnetic Resonance Imaging (MRI) uses magnetic energy and radio waves to create images (“slices”) of the human body.
https://www.amazon.com/Least-Squares-Support-Vector-Machines/dp/9812381511
Least Squares Support Vector Machines [Johan A K Suykens, Tony Van Gestel, Jos De Brabanter, Bart De Moor, Joos Vandewalle] on Amazon.com. *FREE* shipping on qualifying offers. 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 …Reviews: 1
https://books.google.com/books/about/Least_Squares_Support_Vector_Machines.html?id=g8wEimyEmrUC
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 interpretations from optimization theory. The authors explain the natural links between LS-SVM classifiers and kernel Fisher discriminant analysis.
https://www.amazon.com/Least-Squares-Support-Vector-Machines/product-reviews/9812381511
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