Equivalence of Learning Algorithms
Machine Learning
2014-06-11 v1 Machine Learning
Abstract
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.
Keywords
Cite
@article{arxiv.1406.2622,
title = {Equivalence of Learning Algorithms},
author = {Julien Audiffren and Hachem Kadri},
journal= {arXiv preprint arXiv:1406.2622},
year = {2014}
}
Comments
arXiv admin note: substantial text overlap with arXiv:1310.2451