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… 

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