Learning the Kernel for Classification and Regression

TL;DR

This paper explores learning kernels through polynomial combinations to improve regression and classification, supported by numerical experiments on various datasets.

Contribution

It introduces a method for learning kernels via polynomial combinations, enhancing kernel-based learning for regression and classification tasks.

Findings

Polynomial kernel combinations improve model performance.

Numerical experiments demonstrate effectiveness across datasets.

The approach offers a flexible kernel learning framework.

Abstract

We investigate a series of learning kernel problems with polynomial combinations of base kernels, which will help us solve regression and classification problems. We also perform some numerical experiments of polynomial kernels with regression and classification tasks on different datasets.