English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

In this paper we consider the numerical solution to the soft-margin supportvector machine optimization problem. This problem is typically solved using theSMO algorithm, given the high computational complexity of traditionaloptimization algorithms when dealing with large-scale kernel matrices. In thiswork, we propose employing an NFFT-accelerated matrix-vector product using anANOVA decomposition for the feature space that is used within an interior pointmethod for the overall optimization problem. As this method requires thesolution of a linear system of saddle point form we suggest a preconditioningapproach that is based on low-rank approximations of the kernel matrix togetherwith a Krylov subspace solver. We compare the accuracy of the ANOVA-basedkernel with the default LIBSVM implementation. We investigate the performanceof the different preconditioners as well as the accuracy of the ANOVA kernel onseveral large-scale datasets.

A Preconditioned Interior Point Method for Support Vector Machines Using an ANOVA-Decomposition and NFFT-Based Matrix-Vector Products