Sheng Baohuai, Xiang Daohong, "Bound the Learning Rates with Generalized Gradients," WSEAS Transactions on Signal Processing, vol. 8, pp. -, 2012, DOI:
Sheng Baohuai, Xiang Daohong. Bound the Learning Rates with Generalized Gradients.
WSEAS Transactions on Signal Processing. 2012;8:-.
WSEAS Transactions on Signal Processing
Print ISSN: 1790-5052, E-ISSN: 2224-3488
Volume 8, 2012
Bound the Learning Rates with Generalized Gradients
Authors: ,
Abstract: This paper considers the error bounds for the coef cient regularized regression schemes associated with Lipschitz loss. Our main goal is to study the convergence rates for this algorithm with non-smooth analysis. We give an explicit expression of the solution with generalized gradients of the loss which induces a capacity independent bound for the sample error. A kind of approximation error is provided with possibility theory.
Keywords:
Regularization regression, non-smooth analysis, Lipschitz loss, machine learning, learning rates, generalized gradient
Pages: - WSEAS Transactions on Signal Processing, ISSN / E-ISSN: 1790-5052 / 2224-3488, Volume 8, 2012DOI: *As the DOI is a unique identifier, it is already available in the pdf version.
**The DOI link will be activated in the first midst of January 2026.
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