Kernel Combination Through Genetic Programming for Image Classification

Created by W.Langdon from gp-bibliography.bib Revision:1.7970

@InProceedings{conf/ciarp/RibeiroPG15,

• author = "Yuri H. Ribeiro and Zenilton Kleber G. {do Patrocinio Jr.} and Silvio Jamil {Ferzoli Guimaraes}",
• title = "Kernel Combination Through Genetic Programming for Image Classification",
• booktitle = "Progress in Pattern Recognition: Proceedings 20th Iberoamerican Congress on Image Analysis, Computer Vision and Applications {CIARP} 2015",
• publisher = "Springer",
• year = "2015",
• editor = "Alvaro Pardo and Josef Kittler",
• volume = "9423",
• series = "Lecture Notes in Computer Science",
• pages = "314--321",
• address = "Montevideo, Uruguay",
• month = nov # " 9-12",
• keywords = "genetic algorithms, genetic programming, SVM",
• bibdate = "2015-10-27",
• bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/ciarp/ciarp2015.html#RibeiroPG15",
• isbn13 = "978-3-319-25750-1",
• URL = "http://dx.doi.org/10.1007/978-3-319-25751-8",
• size = "8 pages",
• abstract = "Support vector machine is a supervised learning technique which uses kernels to perform nonlinear separations of data. In this work, we propose a combination of kernels through genetic programming in which the individual fitness is obtained by a K-NN classifier using a kernel-based distance measure. Experiments have shown that our method KGP-K is much faster than other methods during training, but it is still able to generate individuals (i.e., kernels) with competitive performance (in terms of accuracy) to the ones that were produced by other methods. KGP-K produces reasonable kernels to use in the SVM with no knowledge about the distribution of data, even if they could be more complex than the ones generated by other methods and, therefore, they need more time during tests.",

}

Genetic Programming entries for Yuri H Ribeiro Zenilton Kleber G do Patrocinio Jr Silvio Jamil Ferzoli Guimaraes

Citations