Understanding Support Vector Machines with Polynomial Kernels
Paper in proceedings, 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.

Polynomial Kernel

Likelihood Ratio Test

Support Vector Machine

Statistical Moments

Quadratic Discrimination

Interpretation

Author

Rikard Vinge

Chalmers, Electrical Engineering, Signalbehandling och medicinsk teknik, Signal Processing

Tomas McKelvey

Chalmers, Electrical Engineering, Signalbehandling och medicinsk teknik, Signal Processing

European Signal Processing Conference

22195491 (ISSN)

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

Subject Categories

Other Mathematics

Probability Theory and Statistics

Signal Processing

DOI

10.23919/EUSIPCO.2019.8903042

More information

Created

12/11/2019