# Genetic Programming for Kernel-Based Learning with Co-evolving Subsets Selection

@article{Gagn2006GeneticPF, title={Genetic Programming for Kernel-Based Learning with Co-evolving Subsets Selection}, author={Christian Gagn{\'e} and Marc Schoenauer and Mich{\`e}le Sebag and Marco Tomassini}, journal={ArXiv}, year={2006}, volume={abs/cs/0611135} }

Support Vector Machines (SVMs) are well-established Machine Learning (ML) algorithms. They rely on the fact that i) linear learning can be formalized as a well-posed optimization problem; ii) non-linear learning can be brought into linear learning thanks to the kernel trick and the mapping of the initial search space onto a high dimensional feature space. The kernel is designed by the ML expert and it governs the efficiency of the SVM approach. In this paper, a new approach for the automatic…

## 35 Citations

### Tuning and evolution of support vector kernels

- Computer ScienceEvol. Intell.
- 2012

Two solutions for optimizing kernel functions are presented: automated hyperparameter tuning of kernel functions combined with an optimization of pre- and post-processing options by Sequential Parameter Optimization (SPO) and evolving new kernel functions by Genetic Programming (GP).

### Learning SVM with Complex Multiple Kernels Evolved by Genetic Programming

- Computer ScienceInt. J. Artif. Intell. Tools
- 2010

The numerical experiments show that the SVM involving the evolutionary complex multiple kernels perform better than the classic simple kernels and on the considered data sets, the new multiple kernels outperform both the cLMK and eLMK — linear multiple kernels.

### Kernel evolution for support vector classification

- Computer Science2011 IEEE Workshop on Evolving and Adaptive Intelligent Systems (EAIS)
- 2011

A novel approach is proposed to use genetic programming (GP) to design domain-specific and optimal kernel functions for support vector classification (SVC) which automatically adjusts the parameters.

### Improving classification performance of Support Vector Machine by genetically optimising kernel shape and hyper-parameters

- Computer ScienceApplied Intelligence
- 2010

Numerical experiments show that the SVM algorithm, involving the evolutionary kernel of kernels (eKoK) the authors propose, performs better than well-known classic kernels whose parameters were optimised and a state of the art convex linear and an evolutionary linear, respectively, kernel combinations.

### Evolutionary Optimization of Least-Squares Support Vector Machines

- Computer ScienceData Mining
- 2010

Empirical studies show that this model indeed increases the generalization performance of the machine, although this improvement comes at a high computational cost, which suggests that the approach may be justified primarily in applications where prediction errors can have severe consequences, such as in medical settings.

### Evolutionary combination of kernels for nonlinear feature transformation

- Computer ScienceInf. Sci.
- 2014

### Evolving kernels for support vector machine classification

- Computer ScienceGECCO '07
- 2007

A new algorithm, called KGP, is introduced, which finds near-optimal kernels using strongly typed genetic programming and principled kernel closure properties, and shows wide applicability.

### Evolving Gaussian Process kernels from elementary mathematical expressions

- Computer ScienceNeurocomputing
- 2021

### In-depth analysis of SVM kernel learning and its components

- Computer ScienceNeural Computing and Applications
- 2020

This paper identifies all the factors that affect the final performance of support vector machines in relation to the elicitation of kernels and studies the influence each component has on the final classification performance, providing recommendations and insights into the kernel setting for support Vector machines.

### GEEK: Grammatical Evolution for Automatically Evolving Kernel Functions

- Computer Science2017 IEEE Trustcom/BigDataSE/ICESS
- 2017

GEEK is proposed, a Grammatical Evolution approach for automatically Evolving Kernel functions that uses a grammar composed of simple mathematical operations extracted from known kernels and is also able to optimize some of their parameters.

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