LS-SVR as a Bayesian RBF Network

Diego P.P. Mesquita, Luis A. Freitas, Joao P.P. Gomes, Cesar L.C. Mattos

Research output: Contribution to journalArticleScientificpeer-review

4 Citations (Scopus)

Abstract

We show theoretical similarities between the least squares support vector regression (LS-SVR) model with a radial basis functions (RBFs) kernel and maximum a posteriori (MAP) inference on Bayesian RBF networks with a specific Gaussian prior on the regression weights. Although previous articles have pointed out similar expressions between those learning approaches, we explicitly and formally state the existing correspondences. We empirically demonstrate our result by performing computational experiments with standard regression benchmarks. Our findings open a range of possibilities to improve LS-SVR by borrowing strength from well-established developments in Bayesian methodology.

Original languageEnglish
Article number8931012
Pages (from-to)4389-4393
Number of pages5
JournalIEEE Transactions on Neural Networks and Learning Systems
Volume31
Issue number10
DOIs
Publication statusPublished - 1 Oct 2020
MoE publication typeA1 Journal article-refereed

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