Lagrangian support vector regression via unconstrained convex minimization

Neural Networks

Volumn 51, Issue , 2014, Pages 67-79

  Balasundaram, S ^a   Gupta, Deepak ^a   Kapil, ^a  

^a JAWAHARLAL NEHRU UNIVERSITY   (India)

Author keywords

Generalized derivative approach; Smooth approximation; Support vector regression; Unconstrained convex minimization

Indexed keywords

LAGRANGE MULTIPLIERS; REGRESSION ANALYSIS;

COMPUTATIONAL RESULTS; CONVEX MINIMIZATION; GENERALIZATION PERFORMANCE; GENERALIZED DERIVATIVES; OPTIMIZATION PROBLEMS; SMOOTH APPROXIMATION; SUPPORT VECTOR REGRESSION (SVR); UNCONSTRAINED MINIMIZATION PROBLEM;

PROBLEM SOLVING;

ACCURACY; ARTICLE; CONTROLLED STUDY; CONVEX MINIMIZATION; FACTUAL DATABASE; GENERALIZED DERIVATIVE APPROACH; GENERALIZED DERIVATIVE UNCONSTRAINED LAGRANGIAN SUPPORT VECTOR REGRESSION; INTERMETHOD COMPARISON; KERNEL METHOD; LEARNING ALGORITHM; LEAST SQUARES SUPPORT VECTOR REGRESSION; NEWTON ITERATIVE ALGORITHM; NEWTON ITERATIVE ALGORITHM FOR SOLVING UNCONSTRAINED LAGRANGIAN SUPPORT VECTOR REGRESSION; PRIORITY JOURNAL; PROCESS OPTIMIZATION; QUALITY CONTROL; REGRESSION ANALYSIS; STATISTICAL ANALYSIS; STATISTICAL MODEL; SUPPORT VECTOR MACHINE; UNCONSTRAINED LAGRANGIAN SUPPORT VECTOR REGRESSION;

GENERALIZED DERIVATIVE APPROACH; SMOOTH APPROXIMATION; SUPPORT VECTOR REGRESSION; UNCONSTRAINED CONVEX MINIMIZATION;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; DATABASES AS TOPIC; ENGINEERING; LINEAR MODELS; NONLINEAR DYNAMICS; NORMAL DISTRIBUTION; PRESSURE; REGRESSION ANALYSIS; ROBOTICS; SOFTWARE; SUPPORT VECTOR MACHINES; TIME FACTORS;

EID: 84891335988     PISSN: 08936080     EISSN: 18792782     Source Type: Journal    
DOI: 10.1016/j.neunet.2013.12.003     Document Type: Article

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