# A Quasi-Newton Method for Large Scale Support Vector Machines

arXiv: https://arxiv.org/abs/1402.4861 · HTML: https://mlsanity.com/paper/1402.4861/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Feb 2014.

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

- [Complexity-Optimized Sparse Bayesian Learning for Scalable Classification Tasks](https://mlsanity.com/paper/2107.08195/) (2021)

## Abstract (arXiv)

This paper adapts a recently developed regularized stochastic version of the Broyden, Fletcher, Goldfarb, and Shanno (BFGS) quasi-Newton method for the solution of support vector machine classification problems. The proposed method is shown to converge almost surely to the optimal classifier at a rate that is linear in expectation. Numerical results show that the proposed method exhibits a convergence rate that degrades smoothly with the dimensionality of the feature vectors.