WSEAS Transactions on Signal Processing
Print ISSN: 1790-5052, E-ISSN: 2224-3488
Volume 8, 2012
Bound the Learning Rates with Generalized Gradients
Authors: Sheng Baohuai, Xiang Daohong
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.