R-CTSVM+: Robust capped L1-norm twin support vector machine with privileged information

2021 ◽  
Author(s):  
Yanmeng Li ◽  
Huaijiang Sun ◽  
Wenzhu Yan ◽  
Qiongjie Cui
Keyword(s):  
Support Vector Machine ◽  
Twin Support Vector Machine ◽  
Support Vector ◽  
Privileged Information ◽  
L1 Norm

Robust capped L1-norm twin support vector machine

2019 ◽  
Vol 114 ◽  
pp. 47-59 ◽  
Author(s):  
Chunyan Wang ◽  
Qiaolin Ye ◽  
Peng Luo ◽  
Ning Ye ◽  
Liyong Fu
Keyword(s):  
Support Vector Machine ◽  
Twin Support Vector Machine ◽  
Support Vector ◽  
L1 Norm

L1-norm loss-based projection twin support vector machine for binary classification

2019 ◽  
Vol 23 (21) ◽  
pp. 10649-10659 ◽  
Author(s):  
Xiaopeng Hua ◽  
Sen Xu ◽  
Jun Gao ◽  
Shifei Ding
Keyword(s):  
Support Vector Machine ◽  
Binary Classification ◽  
Twin Support Vector Machine ◽  
Support Vector ◽  
L1 Norm

scholarly journals Intuitionistic Fuzzy Laplacian Twin Support Vector Machine for Semi-supervised Classification

2021 ◽  
Author(s):  
Jia-Bin Zhou ◽  
Yan-Qin Bai ◽  
Yan-Ru Guo ◽  
Hai-Xiang Lin
Keyword(s):  
Support Vector Machine ◽  
Negative Impact ◽  
Twin Support Vector Machine ◽  
Fuzzy Membership ◽  
Support Vector ◽  
Membership Functions ◽  
Fuzzy Membership Functions ◽  
Intuitionistic Fuzzy ◽  
Benchmark Datasets ◽  
The Impact

AbstractIn general, data contain noises which come from faulty instruments, flawed measurements or faulty communication. Learning with data in the context of classification or regression is inevitably affected by noises in the data. In order to remove or greatly reduce the impact of noises, we introduce the ideas of fuzzy membership functions and the Laplacian twin support vector machine (Lap-TSVM). A formulation of the linear intuitionistic fuzzy Laplacian twin support vector machine (IFLap-TSVM) is presented. Moreover, we extend the linear IFLap-TSVM to the nonlinear case by kernel function. The proposed IFLap-TSVM resolves the negative impact of noises and outliers by using fuzzy membership functions and is a more accurate reasonable classifier by using the geometric distribution information of labeled data and unlabeled data based on manifold regularization. Experiments with constructed artificial datasets, several UCI benchmark datasets and MNIST dataset show that the IFLap-TSVM has better classification accuracy than other state-of-the-art twin support vector machine (TSVM), intuitionistic fuzzy twin support vector machine (IFTSVM) and Lap-TSVM.

Robust Parametric Twin Support Vector Machine for Pattern Classification

2017 ◽  
Vol 47 (1) ◽  
pp. 293-323 ◽  
Author(s):  
Reshma Rastogi ◽  
Sweta Sharma ◽  
Suresh Chandra
Keyword(s):  
Support Vector Machine ◽  
Pattern Classification ◽  
Twin Support Vector Machine ◽  
Support Vector
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