Strip Steel Surface Defect Classification Method Based on Enhanced Twin Support Vector Machine

ISIJ International ◽

10.2355/isijinternational.54.119 ◽

2014 ◽

Vol 54 (1) ◽

pp. 119-124 ◽

Author(s):

Maoxiang Chu ◽

Rongfen Gong ◽

Anna Wang

Keyword(s):

Support Vector Machine ◽

Steel Surface ◽

Surface Defect ◽

Classification Method ◽

Twin Support Vector Machine ◽

Support Vector ◽

Defect Classification ◽

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Strip Steel Surface Defect Recognition Based on Novel Feature Extraction and Enhanced Least Squares Twin Support Vector Machine

ISIJ International ◽

10.2355/isijinternational.54.1638 ◽

2014 ◽

Vol 54 (7) ◽

pp. 1638-1645 ◽

Author(s):

Maoxiang Chu ◽

Anna Wang ◽

Rongfen Gong ◽

Mo Sha

Keyword(s):

Support Vector Machine ◽

Feature Extraction ◽

Least Squares ◽

Steel Surface ◽

Surface Defect ◽

Twin Support Vector Machine ◽

Support Vector ◽

Strip Steel ◽

Defect Recognition

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Multi-class classification method for strip steel surface defects based on support vector machine with adjustable hyper-sphere

Journal of Iron and Steel Research International ◽

10.1007/s42243-018-0103-6 ◽

2018 ◽

Vol 25 (7) ◽

pp. 706-716 ◽

Author(s):

Mao-xiang Chu ◽

Xiao-ping Liu ◽

Rong-fen Gong ◽

Jie Zhao

Keyword(s):

Support Vector Machine ◽

Steel Surface ◽

Surface Defects ◽

Classification Method ◽

Support Vector ◽

Strip Steel ◽

Multi Class Classification

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Steel surface defect classification using multiple hyper-spheres support vector machine with additional information

Chemometrics and Intelligent Laboratory Systems ◽

10.1016/j.chemolab.2017.11.018 ◽

2018 ◽

Vol 172 ◽

pp. 109-117 ◽

Author(s):

Rongfen Gong ◽

Chengdong Wu ◽

Maoxiang Chu

Keyword(s):

Support Vector Machine ◽

Steel Surface ◽

Surface Defect ◽

Support Vector ◽

Defect Classification ◽

Additional Information

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Steel surface defects recognition based on multi-type statistical features and enhanced twin support vector machine

Chemometrics and Intelligent Laboratory Systems ◽

10.1016/j.chemolab.2017.10.020 ◽

2017 ◽

Vol 171 ◽

pp. 140-150 ◽

Author(s):

Maoxiang Chu ◽

Rongfen Gong ◽

Song Gao ◽

Jie Zhao

Keyword(s):

Support Vector Machine ◽

Steel Surface ◽

Surface Defects ◽

Twin Support Vector Machine ◽

Support Vector ◽

Statistical Features

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The Strip Steel Surface Defect Recognition Based on Multiple Support Vector Hyper-Sphere with Feature and Sample Weights

steel research international ◽

10.1002/srin.201600049 ◽

2016 ◽

Vol 87 (12) ◽

pp. 1678-1685 ◽

Author(s):

Rongfen Gong ◽

Chengdong Wu ◽

Maoxiang Chu ◽

Xiaoping Liu

Keyword(s):

Steel Surface ◽

Surface Defect ◽

Support Vector ◽

Strip Steel ◽

Sample Weights ◽

Multiple Support ◽

Defect Recognition

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Multi-class Classification Method Based on Support Vector Machine with Hyper-sphere for Steel surface Defects

2018 37th Chinese Control Conference (CCC) ◽

10.23919/chicc.2018.8483656 ◽

2018 ◽

Author(s):

Rongfen Gong ◽

Chengdong Wu ◽

Maoxiang Chu

Keyword(s):

Support Vector Machine ◽

Steel Surface ◽

Surface Defects ◽

Classification Method ◽

Support Vector ◽

Multi Class Classification

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Unbalanced classification method using least squares support vector machine with sparse strategy for steel surface defects with label noise

Journal of Iron and Steel Research International ◽

10.1007/s42243-020-00499-6 ◽

2020 ◽

Vol 27 (12) ◽

pp. 1407-1419

Author(s):

Li-ming Liu ◽

Mao-xiang Chu ◽

Rong-fen Gong ◽

Xin-yu Qi

Keyword(s):

Support Vector Machine ◽

Least Squares ◽

Steel Surface ◽

Surface Defects ◽

Classification Method ◽

Support Vector ◽

Label Noise ◽

Unbalanced Classification

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Strip Steel Surface Defects Detection Based on Low Rank Multi-Kernel Support Vector Machine

Journal of Computational and Theoretical Nanoscience ◽

10.1166/jctn.2016.5515 ◽

2016 ◽

Vol 13 (9) ◽

pp. 5975-5981

Author(s):

Hou Jingzhong ◽

Xia Kewen ◽

Yang Fan ◽

Cui Dongyan

Keyword(s):

Support Vector Machine ◽

Steel Surface ◽

Surface Defects ◽

Low Rank ◽

Support Vector ◽

Strip Steel ◽

Kernel Support Vector Machine

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Automated Diagnosis system for detection of the pathological brain using Fast version of Simplified Pulse-Coupled Neural Network and Twin Support Vector Machine

Multimedia Tools and Applications ◽

10.1007/s11042-021-10937-6 ◽

2021 ◽

Author(s):

Ravi Shanker ◽

Mahua Bhattacharya

Keyword(s):

Neural Network ◽

Support Vector Machine ◽

Twin Support Vector Machine ◽

Support Vector ◽

Automated Diagnosis ◽

Diagnosis System ◽

Pulse Coupled Neural Network

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Robust truncated L$$_2$$-norm twin support vector machine

International Journal of Machine Learning and Cybernetics ◽

10.1007/s13042-021-01368-8 ◽

2021 ◽

Author(s):

Linxi Yang ◽

Guoquan Li ◽

Zhiyou Wu ◽

Changzhi Wu

Keyword(s):

Support Vector Machine ◽

Twin Support Vector Machine ◽

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