Algorithms for the Training of Neural Support Vector Machines

TL;DR

This paper introduces new training algorithms for neural support vector machines that incorporate domain knowledge and adapt existing methods like Pegasos to improve learning on standard tasks.

Contribution

It presents novel training algorithms for NSVMs based on Pegasos, demonstrating their effectiveness on benchmark machine learning problems.

Findings

Successful adaptation of Pegasos for NSVM training

Effective incorporation of domain knowledge into NSVMs

Promising results on standard machine learning tasks

Abstract

Neural support vector machines (NSVMs) allow for the incorporation of domain knowledge in the design of the model architecture. In this article we introduce a set of training algorithms for NSVMs that leverage the Pegasos algorithm and provide a proof of concept by solving a set of standard machine learning tasks.