Genetic programming (videotape): the movie
Genetic programming (videotape): the movie
Computers and Operations Research
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
The Genetic Kernel Support Vector Machine: Description and Evaluation
Artificial Intelligence Review
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Evolving kernels for support vector machine classification
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Evolving Kernel Functions for SVMs by Genetic Programming
ICMLA '07 Proceedings of the Sixth International Conference on Machine Learning and Applications
Kernel Trees for Support Vector Machines
IEICE - Transactions on Information and Systems
Genetic programming for kernel-based learning with co-evolving subsets selection
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
IWINAC'05 Proceedings of the First international work-conference on the Interplay Between Natural and Artificial Computation conference on Artificial Intelligence and Knowledge Engineering Applications: a bioinspired approach - Volume Part II
IWANN'05 Proceedings of the 8th international conference on Artificial Neural Networks: computational Intelligence and Bioinspired Systems
Applying Evolutionary Computation Methods to Formal Testing and Model Checking
IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part I: Bio-Inspired Systems: Computational and Ambient Intelligence
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Although there is a large diversity in the literature related to kernel methods, there are only a few works which do not use kernels based on Radial Basis Functions (RBF) for regression problems. The reason for that is that they present very good generalization capabilities and smooth interpolation. This paper studies an initial framework to create specific-to-problem kernels for application to regression models. The kernels are created without prior knowledge about the data to be approximated by means of a Genetic Programming algorithm. The quality of a kernel is evaluated independently of a particular model, using a modified version of a non parametric noise estimator. For a particular problem, performances of generated kernels are tested against common ones using weighted k-nn in the kernel space. Results show that the presented method produces specific-to-problem kernels that outperform the common ones for this particular case. Parallel programming is utilized to deal with large computational costs.