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Equivalence of Learning Algorithms
lib:674bea106388a4d5 (v1.0.0)
Authors: Julien Audiffren,Hachem Kadri
ArXiv: 1406.2622
Document: PDF DOI
Abstract URL: http://arxiv.org/abs/1406.2622v1
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.
ArXiv: 1406.2622
Document: PDF DOI
Abstract URL: http://arxiv.org/abs/1406.2622v1
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.
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