# Algorithms for the Training of Neural Support Vector Machines

arXiv: https://arxiv.org/abs/2308.07204 · HTML: https://mlsanity.com/paper/2308.07204/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 13 references that are in the map.
- First posted on arXiv 14 Aug 2023.

## Builds on (most cited first, 2 of 2)

- [Deep Learning using Linear Support Vector Machines](https://mlsanity.com/paper/1306.0239/) (2013)
- [Quantum Kernel Alignment with Stochastic Gradient Descent](https://mlsanity.com/paper/2304.09899/) (2023)

## Built on by (most cited first, 1 of 1)

- [On Neural Quantum Support Vector Machines](https://mlsanity.com/paper/2308.08467/) (2023)

## Abstract (arXiv)

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.