  The purpose of this paper is to introduce a concept of equivalence between
machine learning algorithms. We define two notions of algorithmic equivalence,
namely, weak and strong equivalence. These notions are of paramount importance
for identifying when learning prop erties from one learning algorithm can be
transferred to another. Using regularized kernel machines as a case study, we
illustrate the importance of the introduced equivalence concept by analyzing
the relation between kernel ridge regression (KRR) and m-power regularized
least squares regression (M-RLSR) algorithms.
