%0 Conference Paper %1 1277332 %A Diosan, Laura %A Oltean, Mihai %A Rogozan, Alexandrina %A Pecuchet, Jean Pierre %B GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation %C London %D 2007 %E Thierens, Dirk %E Beyer, Hans-Georg %E Bongard, Josh %E Branke, Jurgen %E Clark, John Andrew %E Cliff, Dave %E Congdon, Clare Bates %E Deb, Kalyanmoy %E Doerr, Benjamin %E Kovacs, Tim %E Kumar, Sanjeev %E Miller, Julian F. %E Moore, Jason %E Neumann, Frank %E Pelikan, Martin %E Poli, Riccardo %E Sastry, Kumara %E Stanley, Kenneth Owen %E Stutzle, Thomas %E Watson, Richard A %E Wegener, Ingo %I ACM Press %K Genetics-Based Learning: Machine Machines, Poster, SVM Support Vector algorithms, genetic kernel, programming, %P 1873--1873 %T Genetically designed multiple-kernels for improving the SVM performance %U http://doi.acm.org/10.1145/1276958.1277332 %V 2 %X Classical kernel-based classifiers only use a single kernel, but the real-world applications have emphasised the need to consider a combination of kernels - also known as a multiple kernel - in order to boost the performance. Our purpose is to automatically find the mathematical expression of a multiple kernel by evolutionary means. In order to achieve this purpose we propose a hybrid model that combines a Genetic Programming (GP) algorithm and a kernel-based Support Vector Machine (SVM) classifier. Each GP chromosome is a tree encoding the mathematical expression of a multiple kernel. Numerical experiments show that the SVM embedding the evolved multiple kernel performs better than the standard kernels for the considered classification problems.

@inproceedings{1277332, abstract = {Classical kernel-based classifiers only use a single kernel, but the real-world applications have emphasised the need to consider a combination of kernels - also known as a multiple kernel - in order to boost the performance. Our purpose is to automatically find the mathematical expression of a multiple kernel by evolutionary means. In order to achieve this purpose we propose a hybrid model that combines a Genetic Programming (GP) algorithm and a kernel-based Support Vector Machine (SVM) classifier. Each GP chromosome is a tree encoding the mathematical expression of a multiple kernel. Numerical experiments show that the SVM embedding the evolved multiple kernel performs better than the standard kernels for the considered classification problems.}, added-at = {2008-06-19T17:35:00.000+0200}, address = {London}, author = {Diosan, Laura and Oltean, Mihai and Rogozan, Alexandrina and Pecuchet, Jean Pierre}, biburl = {https://www.bibsonomy.org/bibtex/21fd8a7c6de2cfbc206b6dfc50a860470/brazovayeye}, booktitle = {GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation}, editor = {Thierens, Dirk and Beyer, Hans-Georg and Bongard, Josh and Branke, Jurgen and Clark, John Andrew and Cliff, Dave and Congdon, Clare Bates and Deb, Kalyanmoy and Doerr, Benjamin and Kovacs, Tim and Kumar, Sanjeev and Miller, Julian F. and Moore, Jason and Neumann, Frank and Pelikan, Martin and Poli, Riccardo and Sastry, Kumara and Stanley, Kenneth Owen and Stutzle, Thomas and Watson, Richard A and Wegener, Ingo}, interhash = {dffcf2b8cf36b4d8410df0f4725234eb}, intrahash = {1fd8a7c6de2cfbc206b6dfc50a860470}, isbn13 = {978-1-59593-697-4}, keywords = {Genetics-Based Learning: Machine Machines, Poster, SVM Support Vector algorithms, genetic kernel, programming,}, month = {7-11 July}, notes = {GECCO-2007 A joint meeting of the sixteenth international conference on genetic algorithms (ICGA-2007) and the twelfth annual genetic programming conference (GP-2007). ACM Order Number 910071 Evolved SVM Kernel forced to remain valid because only legal combinations (ie plus, times, exp) of legal kernels (linear, plynomial, RBF) is allowed.}, organisation = {ACM SIGEVO (formerly ISGEC)}, pages = {1873--1873}, publisher = {ACM Press}, publisher_address = {New York, NY, USA}, timestamp = {2008-06-19T17:38:46.000+0200}, title = {Genetically designed multiple-kernels for improving the {SVM} performance}, url = {http://doi.acm.org/10.1145/1276958.1277332}, volume = 2, year = 2007 }