Learning from regularized regression algorithms with <Emphasis Type="Italic">p</Emphasis>-order Markov chain sampling

Evaluation for the performance of learning algorithm has been the main thread of theoretical research of machine learning. The performance of the regularized regression algorithm based on independent and identically distributed(i.i.d.) samples has been researched by a large number of references. In the present paper we provide the convergence rates for the performance of regularized regression based on the inputs of p-order Markov chains.