Evolving kernel functions for SVMs by genetic programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.8010
- @InProceedings{Diosan:2007:ICMLA,
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title = "Evolving kernel functions for SVMs by genetic
programming",
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author = "Laura Diosan and Alexandrina Rogozan and
Jean-Pierre Pecuchet",
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booktitle = "Sixth International Conference on Machine Learning and
Applications, ICMLA 2007",
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year = "2007",
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month = "13-15 " # dec,
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pages = "19--24",
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address = "Cincinnati, Ohio, USA",
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publisher = "IEEE",
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keywords = "genetic algorithms, genetic programming, support
vector machines, SVM, GP chromosome, SVM kernel
functions, evolved kernel, kernel expression,
mathematical expression, tree encoding",
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isbn13 = "978-0-7695-3069-7",
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DOI = "doi:10.1109/ICMLA.2007.70",
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abstract = "hybrid model for evolving support vector machine (SVM)
kernel functions is developed in this paper. The kernel
expression is considered as a parameter of the SVM
algorithm and the current approach tries to find the
best expression for this SVM parameter. The model is a
hybrid technique that combines a genetic programming
(GP) algorithm and a support vector machine (SVM)
algorithm. Each GP chromosome is a tree encoding the
mathematical expression for the kernel function. The
evolved kernel is compared to several human-designed
kernels and to a previous genetic kernel on several
datasets. Numerical experiments show that the SVM
embedding our evolved kernel performs statistically
better than standard kernels, but also than previous
genetic kernel for all considered classification
problems.",
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notes = "also known as \cite{4457202}.
http://www.icmla-conference.org/icmla07/",
- }
Genetic Programming entries for
Laura Diosan
Alexandrina Rogozan
Jean Pierre Pecuchet
Citations