# Dual SVM Training on a Budget

arXiv: https://arxiv.org/abs/1806.10182 · HTML: https://mlsanity.com/paper/1806.10182/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Jun 2018.

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

- [Recipe for Fast Large-scale SVM Training: Polishing, Parallelism, and more RAM!](https://mlsanity.com/paper/2207.01016/) (2022)

## Abstract (arXiv)

We present a dual subspace ascent algorithm for support vector machine training that respects a budget constraint limiting the number of support vectors. Budget methods are effective for reducing the training time of kernel SVM while retaining high accuracy. To date, budget training is available only for primal (SGD-based) solvers. Dual subspace ascent methods like sequential minimal optimization are attractive for their good adaptation to the problem structure, their fast convergence rate, and their practical speed. By incorporating a budget constraint into a dual algorithm, our method enjoys the best of both worlds. We demonstrate considerable speed-ups over primal budget training methods.