Understanding Support Vector Machines with Polynomial Kernels
Paper i proceeding, 2019

Interpreting models learned by a support vector machine (SVM) is often difficult, if not impossible, due to working in high-dimensional spaces. In this paper, we present an investigation into polynomial kernels for the SVM. We show that the models learned by these machines are constructed from terms related to the statistical moments of the support vectors. This allows us to deepen our understanding of the internal workings of these models and, for example, gauge the importance of combinations of features. We also discuss how the SVM with a quadratic kernel is related to the likelihood-ratio test for normally distributed populations.

Statistical Moments

Support Vector Machine

Quadratic Discrimination

Likelihood Ratio Test

Interpretation

Polynomial Kernel

Författare

Rikard Vinge

Chalmers, Elektroteknik, Signalbehandling och medicinsk teknik

Tomas McKelvey

Chalmers, Elektroteknik, Signalbehandling och medicinsk teknik

European Signal Processing Conference

22195491 (ISSN)

Vol. 2019-September
9789082797039 (ISBN)

2019 27th European Signal Processing Conference (EUSIPCO)
A Coruna, Spain,

Ämneskategorier

Annan matematik

Sannolikhetsteori och statistik

Signalbehandling

DOI

10.23919/EUSIPCO.2019.8903042

ISBN

9789082797039

Mer information

Senast uppdaterat

2022-04-05