Feature selection for least squares projection twin support vector machine

Neurocomputing

Volumn 144, Issue , 2014, Pages 174-183

  Guo, Jianhui ^a   Yi, Ping ^b   Wang, Ruili ^c   Ye, Qiaolin ^d   Zhao, Chunxia ^a  

^a NANJING UNIVERSITY OF SCIENCE AND TECHNOLOGY   (China)

^b SOUTHEAST UNIVERSITY   (China)

^c MASSEY UNIVERSITY   (New Zealand)

^d NANJING FORESTRY UNIVERSITY   (China)

Author keywords

Feature selection; Least squares projection twin support vector machine; Twin support vector machine

Indexed keywords

CLASSIFICATION (OF INFORMATION); QUADRATIC PROGRAMMING; SUPPORT VECTOR MACHINES; VECTORS;

BINARY CLASSIFICATION; CLASSIFICATION ACCURACY; FEATURE SUPPRESSION; NONLINEAR CLASSIFIERS; QUADRATIC PROGRAMMING PROBLEMS; SINGULARITY PROBLEMS; TIKHONOV REGULARIZATION; TWIN SUPPORT VECTOR MACHINES;

FEATURE EXTRACTION;

ACCURACY; ARTICLE; CLASSIFICATION; CLASSIFIER; DATA BASE; FEATURE SELECTION FOR LEAST SQUARES PROJECTION TWIN SUPPORT VECTOR MACHINE; KERNEL METHOD; LEAST SQUARES PROJECTION TWIN SUPPORT VECTOR MACHINE; MATHEMATICAL ANALYSIS; MATHEMATICAL COMPUTING; NONLINEAR SYSTEM; PRIORITY JOURNAL; REGRESSION ANALYSIS; STANDARDIZATION; SUPPORT VECTOR MACHINE; TWIN SUPPORT VECTOR MACHINE;

EID: 84906064923     PISSN: 09252312     EISSN: 18728286     Source Type: Journal    
DOI: 10.1016/j.neucom.2014.05.040     Document Type: Article

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