Incremental learning for ν-Support Vector Regression

Neural Networks

Volumn 67, Issue , 2015, Pages 140-150

  Gu, Bin ^a,b,c   Sheng, Victor S ^d   Wang, Zhijie ^e   Ho, Derek ^f   Osman, Said ^g   Li, Shuo ^c,e  

^a NANJING UNIVERSITY OF INFORMATION SCIENCE AND TECHNOLOGY   (China)

^b Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology   (China)

^c UNIVERSITY OF WESTERN ONTARIO   (Canada)

^d UNIVERSITY OF CENTRAL ARKANSAS   (United States)

^e GE Health Care   (Canada)

^f LONDON HEALTH SCIENCES CENTRE   (Canada)

^g ST JOSEPH S HEALTH CENTRE   (Canada)

Author keywords

Incremental learning; Online learning; Support vector machine; Support Vector Regression

Indexed keywords

ALGORITHMS; REGRESSION ANALYSIS; SOCIAL NETWORKING (ONLINE); SUPPORT VECTOR MACHINES; VECTORS;

BENCHMARK DATASETS; INCREMENTAL LEARNING; KARUSH-KUHN-TUCKER CONDITION; OBJECTIVE FUNCTIONS; ONLINE LEARNING; OPTIMAL SOLUTIONS; SUPPORT VECTOR CLASSIFICATION; SUPPORT VECTOR REGRESSION (SVR);

LEARNING ALGORITHMS;

ALGORITHM; ARTICLE; COMPARATIVE STUDY; CONFLICT; CONTROL SYSTEM; EXPERIMENTAL DESIGN; INCREMENTAL V SVR LEARNING ALGORITHM; INFORMATION PROCESSING; INITIAL ADJUSTMENT; KARUSH KUHN TUCKER CONDITION; KERNEL METHOD; MACHINE LEARNING; MATHEMATICAL ANALYSIS; PRIORITY JOURNAL; PROCESS DEVELOPMENT; QUALITY CONTROL; RELAXED ADIABATIC INCREMENTAL ADJUSTMENT; STRICT RESTORATION ADJUSTMENT; SUPPORT VECTOR MACHINE; THEORETICAL STUDY; V SUPPORT VECTOR REGRESSION; HUMAN; NORMAL DISTRIBUTION; STATISTICAL MODEL;

ALGORITHMS; HUMANS; LINEAR MODELS; MACHINE LEARNING; NORMAL DISTRIBUTION; SUPPORT VECTOR MACHINE;

EID: 84929471718     PISSN: 08936080     EISSN: 18792782     Source Type: Journal    
DOI: 10.1016/j.neunet.2015.03.013     Document Type: Article

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