NESVM: A fast gradient method for support vector machines

Proceedings - IEEE International Conference on Data Mining, ICDM

Volumn , Issue , 2010, Pages 679-688

  Zhou, Tianyi ^a   Tao, Dacheng ^a   Wu, Xindong ^b,c  

^a UNIVERSITY OF TECHNOLOGY SYDNEY   (Australia)

^b HEFEI UNIVERSITY OF TECHNOLOGY   (China)

^c University of Vermont ^*  (United States)

Author keywords

1 norm; Continuation method; Hinge loss; Nesterov's method; Smooth; Support vector machines

Indexed keywords

CONTINUATION METHOD; CONVERGENCE RATES; EVENT RECOGNITION; HOMOTOPIES; INDOOR/OUTDOOR; LEAST SQUARE; LINE SEARCHES; LINEAR TIME COMPLEXITY; LIPSCHITZ CONSTANT; MATLAB CODE; MATRIX VECTOR MULTIPLICATION; NESTEROV'S METHOD; NON-DIFFERENTIABLE; NONLINEAR KERNELS; NUMBER OF ITERATIONS; OPTIMAL CONVERGENCE; SCENE CLASSIFICATION; SCENE RECOGNITION; SMOOTH; STEP SIZE; SVM MODEL; SVM SOLVERS;

ARTIFICIAL INTELLIGENCE; DATA MINING; GRADIENT METHODS; LINEAR PROGRAMMING; MATLAB; OPTIMIZATION; VECTORS;

SUPPORT VECTOR MACHINES;

EID: 79951751439     PISSN: 15504786     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICDM.2010.135     Document Type: Conference Paper

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