# Randomized Kernel Methods for Least-Squares Support Vector Machines

## TL;DR

This paper introduces randomized kernel approximation algorithms for least-squares support vector machines, enhancing scalability and accuracy in multi-class classification tasks with large datasets.

## Contribution

It proposes novel randomized block kernel matrix methods that improve scalability and maintain accuracy for large-scale multi-class SVM classification.

## Key findings

- Good accuracy achieved with randomized methods
- Reliable scaling for large datasets
- Effective in multi-class classification

## Abstract

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.