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https://www.sciencedirect.com/science/article/pii/S0925231211003808
In this paper we propose a novel feature selection method based on LSTSVM, termed as 1-Norm Least Squares Twin Support Vector Machines (NELSTSVM). A simple technique used in NELSTSVM is to apply a Tikhonov regularization term that is often used to regularize least squares . Then, we easily convert this formulation to a standard LP by replacing ...Cited by: 57
https://dl.acm.org/doi/10.1016/j.neucom.2011.06.015
During the last few years, nonparallel plane classifiers, such as Multisurface Proximal Support Vector Machine via Generalized Eigenvalues (GEPSVM), and Least Squares TWSVM (LSTSVM), ... 1-Norm least squares twin support vector machines.
https://www.researchgate.net/publication/241101100_1Norm_least_squares_twin_support_vector_machines
In 2011, Shangbing Gao et al. [28] proposed 1-norm least squares twin support vector machines (NELSTSVMs). NELSTSVMs have the ability to select the input features automatically. ...
https://dl.acm.org/citation.cfm?id=2305137
We're upgrading the ACM DL, and would like your input. Please sign up to review new features, functionality and page designs.Cited by: 57
https://www.sciencedirect.com/science/article/pii/S0031320317303874
Inspired by the advantages of least squares twin support vector machine (LSTWSVM), TBSVM and L1-norm distance, we propose a LSTBSVM based on L1-norm distance metric for binary classification, termed as L1-LSTBSVM, which is specially designed for suppressing the negative effect of outliers and improving computational efficiency in large datasets.Cited by: 24
https://www.sciencedirect.com/science/article/pii/S0957417408006854
In this paper we formulate a least squares version of the recently proposed twin support vector machine (TSVM) for binary classification. This formulation leads to extremely simple and fast algorithm for generating binary classifiers based on two non-parallel hyperplanes.Cited by: 446
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.iis.sinica.edu.tw/page/jise/2014/201411_06.pdf
2.2 Least Squares Twin Support Vector Machine To further improve the computational speed of classifier, LS-TSVM [8] was pro-posed in the spirit of TSVM, and it seeks to solve a pair of smaller-sized QPPs rather than a single large-sized one as in LS-SVM. The illustration of the least squares TSVM is shown as Fig. 2. Fig. 2.
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 Machine ...Cited by: 7
https://www.researchgate.net/publication/320025783_Least_squares_twin_bounded_support_vector_machines_based_on_L1-norm_distance_metric_for_classification
Inspired by the advantages of least squares twin support vector machine (LSTWSVM), TBSVM and L1-norm distance, we propose a LSTBSVM based on L1-norm distance metric for binary classification ...
https://link.springer.com/article/10.1007/s10489-014-0586-1
To overcome the above shortcoming, we propose l p norm least square twin support vector machine (l p LSTSVM). Our new model is an adaptive learning procedure with l p -norm (0< p <1), where p is viewed as an adjustable parameter and can be automatically chosen by data.
https://en.wikipedia.org/wiki/Least_Squares_Support_Vector_Machine
Least-squares support-vector machines (LS-SVM) are least-squares versions of support-vector machines (SVM), which are a set of related supervised learning methods that analyze data and recognize patterns, and which are used for classification and regression analysis.In this version one finds the solution by solving a set of linear equations instead of a convex quadratic programming (QP ...
https://www.sciencedirect.com/science/article/pii/S0925231218302923
Furthermore, the Newton method with fast convergence ability was used to solve the problem of external penalty in the linear programming dual problem. Thus, a 1-norm least squares twin support vector machine (NLSTWSVM) learning algorithm that can automatically select a sample feature was proposed.
http://kjc.njfu.edu.cn/uploads/file/20180316/20180316145329_30744.pdf
but L1-norm distance is usually regarded as an alternative to L2-norm to improve model robustness in the of outliers. Inspired by the advantages of least squares twin support vector machine (LST- WSVM), TBSVM and L1-norm distance, we propose a LSTBSVMbased on L1-norm …
https://link.springer.com/chapter/10.1007/978-981-10-3002-4_44
Oct 22, 2016 · We first introduce a Tikhonov regularization term to the objective function of projection twin support vector machine (PTSVM). Then we convert it to a linear programming (LP) problem by replacing all the 2-norm terms in the objective function with 1-norm ones.
https://www.researchgate.net/publication/271658151_Sparse_least_square_twin_support_vector_machine_with_adaptive_norm
To overcome the above shortcoming, we propose l p norm least square twin support vector machine (l p LSTSVM). Our new model is an adaptive learning procedure with l p -norm (0 Do you want to read ...
https://www.massey.ac.nz/~rwang/publications/14-NC-Guo.pdf
Twin Support Vector Machine Least Squares Projection Twin Support Vector Machine Feature selection abstract In this paper, we propose a new feature selection approach for the recently proposed Least Squares Projection Twin Support Vector Machine (LSPTSVM) for binary classification. 1-norm …
https://www.researchgate.net/publication/313659887_1-norm_support_vector_machines
If ρ is a defined (but unknown) probability measure on Z := X × Y , we employ the least squares loss y − f (x) ... The present study used 1-norm support vector machine (SVM) as a ...
https://www.researchgate.net/publication/323660342_Twin_Support_Vector_Machines_A_Survey
Twin support vector machines (TWSVM) is a new machine learning method based on the theory of Support Vector Machine (SVM). Unlike SVM, TWSVM …
https://pdfs.semanticscholar.org/d8e8/50566921b20800da0b9e8a755ad9d73eddec.pdf
Twin support vector machine (TWSVM) was initially designed for binary classification. However, real-world problems often require the discrimination more than two categories. To tackle multi-class classification problem, in this paper, a multiple least squares twin support vector machine is proposed. Our Multi-LSTSVM solves K quadratic
https://deepai.org/publication/fuzzy-least-squares-twin-support-vector-machines
05/20/15 - Least Squares Twin Support Vector Machine (LSTSVM) is an extremely efficient and fast version of SVM algorithm for binary classifi...
https://www.researchgate.net/publication/320150156_Twin_Support_Vector_Machines
This chapter provides an overview of Support Vector Machines and some of its variants. We first discuss \(L_1\)-norm SVM and then proceed to discuss two of the most popular \(L_2\)-norm SVMs ...
https://www.semanticscholar.org/paper/Sparse-least-square-twin-support-vector-machine-Zhang-Zhen/e74fd6c78fe89511ad4acca921ddf5cf03c01dc1
To overcome the above shortcoming, we propose lp norm least square twin support vector machine (lpLSTSVM). Our new model is an adaptive learning procedure with lp-norm (0<p<1), where p is viewed as an adjustable parameter and can be automatically chosen by data.
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