- author = "Tanasanee Phienthrakul and Boonserm Kijsirikul",
- title = "GPES: An Algorithm for Evolving Hybrid Kernel Functions of Support Vector Machines",
- booktitle = "2007 IEEE Congress on Evolutionary Computation",
- year = "2007",
- editor = "Dipti Srinivasan and Lipo Wang",
- pages = "2636--2643",
- address = "Singapore",
- month = "25-28 " # sep,
- organization = "IEEE Computational Intelligence Society",
- publisher = "IEEE Press",
- ISBN = "1-4244-1340-0",
- file = "1717.pdf",
- keywords = "genetic algorithms, genetic programming",
-
DOI = "
doi:10.1109/CEC.2007.4424803",
- abstract = "The Support Vector Machine (SVM) is a popular approach to the classification of data. One problem of SVM is how to choose a kernel and the parameters for the kernel. This paper proposes a classification technique, called GPES, that combines Genetic Programming (GP) and Evolutionary Strategies (ES) to evolve a hybrid kernel for an SVM classifier. The hybrid kernels are represented as trees that have some adjustable parameters. These hybrid kernels are also the Mercer's kernels. The experimental results are compared with a standard SVM classifier using the polynomial and radial basis function kernels with various parameter settings.",
-
notes = "CEC 2007 - A joint meeting of the IEEE, the EPS, and
the IET.
IEEE Catalog Number: 07TH8963C",
