Error Analysis of Least-Squares l^} -Regularized Regression Learning Algorithm With the Non-Identical and Dependent Samples

The selection of the penalty functional is critical for the performance of a regularized learning algorithm, and thus l^{q} -regularizer (1\leq q\leq 2) deserves special attention. We consider the regularized least-squares regression learning algorithm for the non-identical and weakly dependent samples. The dependent samples satisfy the polynomially \beta -mixing condition and the sequence of the non-identical sampling marginal measures converges to a probability measure exponentially in the dual of a Hölder space. We conduct the rigorous unified error analysis and derive the satisfactory learning rates of the algorithm by the stepping stone technique in the error decomposition and the independent-blocks technique in the sample error estimates.