scholarly journals Quadratic surface support vector machine with L1 norm regularization

2021 ◽  
Vol 0 (0) ◽  
pp. 0
Author(s):  
Ahmad Mousavi ◽  
Zheming Gao ◽  
Lanshan Han ◽  
Alvin Lim
Keyword(s):  
Support Vector Machine ◽  
Support Vector ◽  
L1 Norm ◽  
Quadratic Surface

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

Robust L1-norm multi-weight vector projection support vector machine with efficient algorithm

2018 ◽  
Vol 315 ◽  
pp. 345-361 ◽  
Author(s):  
Wei-Jie Chen ◽  
Chun-Na Li ◽  
Yuan-Hai Shao ◽  
Ju Zhang ◽  
Nai-Yang Deng
Keyword(s):  
Support Vector Machine ◽  
Efficient Algorithm ◽  
Weight Vector ◽  
Support Vector ◽  
L1 Norm ◽  
Vector Projection

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 Feature Selection Based on L1-Norm Support Vector Machine and Effective Recognition System for Parkinson’s Disease Using Voice Recordings

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 37718-37734 ◽  
Author(s):  
Amin Ul Haq ◽  
Jian Ping Li ◽  
Muhammad Hammad Memon ◽  
Jalaluddin khan ◽  
Asad Malik ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Support Vector Machine ◽  
Parkinson's Disease ◽  
Feature Selection ◽  
Recognition System ◽  
Support Vector ◽  
L1 Norm

Identification of best discrimination surface by mixed-integer semi-definite programming for support vector machine

2021 ◽  
Author(s):  
Katsuhiro Tanaka ◽  
Rei Yamamoto
Keyword(s):  
Support Vector Machine ◽  
Credit Rating ◽  
Positive Definite ◽  
Mixed Integer ◽  
Support Vector ◽  
Discrimination Accuracy ◽  
Rating Data ◽  
Corporate Credit ◽  
Quadratic Surface ◽  
Corporate Credit Rating

This paper proposes two improvements to the support vector machine (SVM): (i) extension to a semi-positive definite quadratic surface, which improves the discrimination accuracy; (ii) addition of a variable selection constraint. However, this model is formulated as a mixed-integer semi-definite programming (MISDP) problem, and it cannot be solved easily. Therefore, we propose a heuristic algorithm for solving the MISDP problem efficiently and show its effectiveness by using corporate credit rating data.

A proximal quadratic surface support vector machine for semi-supervised binary classification

2017 ◽  
Vol 22 (20) ◽  
pp. 6905-6919 ◽  
Author(s):  
Xin Yan ◽  
Yanqin Bai ◽  
Shu-Cherng Fang ◽  
Jian Luo
Keyword(s):  
Support Vector Machine ◽  
Binary Classification ◽  
Support Vector ◽  
Quadratic Surface
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