# Randomized Kernel Methods for Least-Squares Support Vector Machines

arXiv: https://arxiv.org/abs/1703.07830 · HTML: https://mlsanity.com/paper/1703.07830/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Mar 2017.

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

- [Residual Random Neural Networks](https://mlsanity.com/paper/2410.19987/) (2024)

## Abstract (arXiv)

The least-squares support vector machine is a frequently used kernel method for non-linear regression and classification tasks. Here we discuss several approximation algorithms for the least-squares support vector machine classifier. The proposed methods are based on randomized block kernel matrices, and we show that they provide good accuracy and reliable scaling for multi-class classification problems with relatively large data sets. Also, we present several numerical experiments that illustrate the practical applicability of the proposed methods.