1 Norm Least Squares Twin Support Vector Machines

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1-Norm least squares twin support vector machines ...

    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

1-Norm least squares twin support vector machines ...

    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.

1Norm least squares twin support vector machines Request PDF

    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. ...

1-Norm least squares twin support vector machines

    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

Least squares twin bounded support vector machines based ...

    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

Least squares twin support vector machines for pattern ...

    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

Fuzzy least squares twin support vector machines ...

    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

A Weighted Least Squares Twin Support Vector Machine

    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.

Fuzzy Least Squares Twin 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 Machine ...Cited by: 7

(PDF) Least squares twin bounded support vector machines ...

    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 ...

Sparse least square twin support vector machine with ...

    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.

Least-squares support-vector machine - Wikipedia

    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 ...

Twin support vector machines: A survey - ScienceDirect

    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.

Least squares twin bounded support vector machines …

    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 …

1-Norm Projection Twin Support Vector Machine SpringerLink

    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.

Sparse least square twin support vector machine with ...

    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 ...

Feature selection for least squares projection twin ...

    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 …

1-norm support vector machines Request PDF

    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 ...

(PDF) Twin Support Vector Machines: A Survey

    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 …

Least Squares Twin Support Vector Machine for Multi-Class ...

    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

Fuzzy Least Squares Twin Support Vector Machines DeepAI

    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...

Twin Support Vector Machines Request PDF

    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 ...

Sparse least square twin support vector machine with ...

    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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