A new method for sparsity control in support vector classification and regression

Pattern Recognition

Volumn 34, Issue 1, 2001, Pages 111-125

  Drezet, Pierre M L ^a   Harrison, Robert F ^a  

^a University of Shezeld   (United Kingdom)

Author keywords

Neural network optimisation; Regression; Sparse approximation; Structural risk minimization; Support vector machines

Indexed keywords

CLASSIFICATION (OF INFORMATION); REGRESSION ANALYSIS; SUPPORT VECTOR MACHINES; APPROXIMATION THEORY; COMPUTATIONAL COMPLEXITY; LEARNING ALGORITHMS; NEURAL NETWORKS; VECTORS;

NETWORK OPTIMISATION; PREDICTION FUNCTION; REGRESSION; SPARSE APPROXIMATIONS; STRUCTURAL RISK MINIMIZATION; SUPPORT VECTOR CLASSIFICATION AND REGRESSIONS; SUPPORT VECTOR LEARNING; SUPPORT VECTOR REGRESSION (SVR); SUPPORT VECTOR MACHINES;

VECTORS; PATTERN RECOGNITION;

EID: 0035200758     PISSN: 00313203     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0031-3203(99)00203-4     Document Type: Article

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