%0 Conference Paper %1 Gagne:PPSN:2006 %A Gagne, Christian %A Schoenauer, Marc %A Sebag, Michele %A Tomassini, Marco %B Parallel Problem Solving from Nature - PPSN IX %C Reykjavik, Iceland %D 2006 %E Runarsson, Thomas Philip %E Beyer, Hans-Georg %E Burke, Edmund %E Merelo-Guervos, Juan J. %E Whitley, L. Darrell %E Yao, Xin %I Springer-Verlag %K DSS, algorithms, beagle coevolution, genetic hyperheuristic, open programming, %P 1008--1017 %R doi:10.1007/11844297_102 %T Genetic Programming for Kernel-Based Learning with Co-evolving Subsets Selection %U http://ppsn2006.raunvis.hi.is/proceedings/287.pdf %V 4193 %X Support Vector Machines (SVMs) are well-established Machine Learning (ML) algorithms. They rely on the fact that i) linear learning can be formalised as a well-posed optimisation problem; ii) nonlinear 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 design of kernels by Genetic Programming, called the Evolutionary Kernel Machine (EKM), is presented. EKM combines a well-founded fitness function inspired from the margin criterion, and a co-evolution framework ensuring the computational scalability of the approach. Empirical validation on standard ML benchmark demonstrates that EKM is competitive using state-of-the-art SVMs with tuned hyper-parameters. %@ 3-540-38990-3

@inproceedings{Gagne:PPSN:2006, abstract = {Support Vector Machines (SVMs) are well-established Machine Learning (ML) algorithms. They rely on the fact that i) linear learning can be formalised as a well-posed optimisation problem; ii) nonlinear 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 design of kernels by Genetic Programming, called the Evolutionary Kernel Machine (EKM), is presented. EKM combines a well-founded fitness function inspired from the margin criterion, and a co-evolution framework ensuring the computational scalability of the approach. Empirical validation on standard ML benchmark demonstrates that EKM is competitive using state-of-the-art SVMs with tuned hyper-parameters.}, added-at = {2008-06-19T17:35:00.000+0200}, address = {Reykjavik, Iceland}, author = {Gagne, Christian and Schoenauer, Marc and Sebag, Michele and Tomassini, Marco}, biburl = {https://www.bibsonomy.org/bibtex/2eef5a5cc1ab76a498f137868094afb73/brazovayeye}, booktitle = {Parallel Problem Solving from Nature - PPSN IX}, doi = {doi:10.1007/11844297_102}, editor = {Runarsson, Thomas Philip and Beyer, Hans-Georg and Burke, Edmund and Merelo-Guervos, Juan J. and Whitley, L. Darrell and Yao, Xin}, interhash = {f7ca82b6276c6ca7ab1a4c436ec168af}, intrahash = {eef5a5cc1ab76a498f137868094afb73}, isbn = {3-540-38990-3}, keywords = {DSS, algorithms, beagle coevolution, genetic hyperheuristic, open programming,}, month = {9-13 September}, notes = {PPSN-IX evolved Kernels are forced to be symmetric functions. Mercer's condition not enforced, but evolved. 3 co-evolving populations. runtime < 1 hour. Size based parsimony pressure. Comparison with k-nn nearest neighbours and SVM, GK-SVM (both with somewhat optimised parameters). 6 undemanding UCI benchmarks.}, pages = {1008--1017}, publisher = {Springer-Verlag}, publisher_address = {Berlin}, series = {LNCS}, size = {10 pages}, timestamp = {2008-06-19T17:40:01.000+0200}, title = {Genetic Programming for Kernel-Based Learning with Co-evolving Subsets Selection}, url = {http://ppsn2006.raunvis.hi.is/proceedings/287.pdf}, volume = 4193, year = 2006 }