Learning by atomic norm regularization with polynomial kernels

• DOI: 10.1142/s0219691315500356
• Publication Date: 2015-08-05T02:59:24Z
• Authors: Shouyin Chen; Na Chen
• Keywords: Regularization

In this paper, we propose a learning scheme for regression generated by atomic norm regularization and data independent hypothesis spaces. The spaces based on polynomial kernels are trained from finite datasets, which is of the given sample. We also present an error analysis proposed algorithm with kernels. When dealing algorithms kernels, main difficulty. estimate local reproduction formula. Better estimates derived applying projection iteration techniques. Our study shows that has fast convergence rate O(m ζ-1 ), best in literature.