An efficient regularized K-nearest neighbor based weighted twin support vector regression

Knowledge-Based Systems

Volumn 94, Issue , 2016, Pages 70-87

  Tanveer, M ^a   Shubham, K ^b   Aldhaifallah, M ^c   Ho, S S ^a  

^a NANYANG TECHNOLOGICAL UNIVERSITY   (Singapore)

^b LNM INSTITUTE OF INFORMATION TECHNOLOGY   (India)

^c KING FAHD UNIVERSITY OF PETROLEUM AND MINERALS   (Saudi Arabia)

Author keywords

K nearest neighbor; Machine learning; Newton method; Smoothing techniques; Support vector machines; Twin support vector machines

Indexed keywords

ARTIFICIAL INTELLIGENCE; LEARNING SYSTEMS; MOTION COMPENSATION; NEWTON-RAPHSON METHOD; REGRESSION ANALYSIS; SAMPLING; SUPPORT VECTOR MACHINES; VECTORS;

COMPUTATIONAL COSTS; GENERALIZATION ABILITY; GENERALIZATION PERFORMANCE; K-NEAREST NEIGHBORS; REGULARIZATION TERMS; SMOOTHING TECHNIQUES; SUPPORT VECTOR REGRESSION (SVR); TWIN SUPPORT VECTOR MACHINES;

NEAREST NEIGHBOR SEARCH;

EID: 84953426174     PISSN: 09507051     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.knosys.2015.11.011     Document Type: Article

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