A Simpler Approach to Coefficient Regularized Support Vector Machines Regression
Keyword(s):
We consider a kind of support vector machines regression (SVMR) algorithms associated withlq (1≤q<∞)coefficient-based regularization and data-dependent hypothesis space. Compared with former literature, we provide here a simpler convergence analysis for those algorithms. The novelty of our analysis lies in the estimation of the hypothesis error, which is implemented by setting a stepping stone between the coefficient regularized SVMR and the classical SVMR. An explicit learning rate is then derived under very mild conditions.
Keyword(s):
2016 ◽
Vol 11
(12)
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pp. 1147
Keyword(s):
2010 ◽
Vol 30
(1)
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pp. 236-239
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Keyword(s):
2013 ◽
Vol 9
(3)
◽
pp. 343-347
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