CTSVM: A robust twin support vector machine with correntropy-induced loss function for binary classification problems

Xiaohan Zheng; Li Zhang; Leilei Yan

doi:10.1016/j.ins.2021.01.006

ScienceGate Book Chapters

JOURNAL ARTICLE

CTSVM: A robust twin support vector machine with correntropy-induced loss function for binary classification problems

Xiaohan Zheng Li Zhang Leilei Yan

Year: 2021 Journal: Information Sciences Vol: 559 Pages: 22-45 Publisher: Elsevier BV

DOI: 10.1016/j.ins.2021.01.006

Get Full-Text PDF Get Analytical Report

Keywords:

Hyperplane Support vector machine Binary classification Outlier Robustness (evolution) Hinge loss Computer science Binary number Mathematics Algorithm Bounded function Artificial intelligence Pattern recognition (psychology) Regularization (linguistics) Combinatorics

Metrics

Cited By

4.12

FWCI (Field Weighted Citation Impact)

Refs

0.94

Citation Normalized Percentile

Is in top 1%

Is in top 10%

Citation History

Topics

Advanced Adaptive Filtering Techniques

Physical Sciences → Engineering → Computational Mechanics

Blind Source Separation Techniques

Physical Sciences → Computer Science → Signal Processing

Sparse and Compressive Sensing Techniques

Physical Sciences → Engineering → Computational Mechanics

CTSVM: A robust twin support vector machine with correntropy-induced loss function for binary classification problems

Metrics

Citation History

Topics

Related Documents

Feature Selection Using Sparse Twin Support Vector Machine with Correntropy-Induced Loss

A robust projection twin support vector machine with a generalized correntropy-based loss

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

Robust General Twin Support Vector Machine with Pinball Loss Function

Correntropy-based metric for robust twin support vector machine