Kernel evolution for support vector classification
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- @InProceedings{Alizadeh:2011:EAIS,
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author = "Mehrdad Alizadeh and Mohammad Mehdi Ebadzadeh",
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title = "Kernel evolution for support vector classification",
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booktitle = "IEEE Workshop on Evolving and Adaptive Intelligent
Systems (EAIS 2011)",
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year = "2011",
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month = "11-15 " # apr,
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pages = "93--99",
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address = "Paris",
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size = "7 pages",
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abstract = "Support vector machines (SVMs) have been used in a
variety of classification tasks. SVMs undoubtedly are
one of the most effective classifiers in several data
mining applications. Determination of a kernel function
and related parameters has been a bottleneck for this
group of classifiers. In this paper a novel approach is
proposed to use genetic programming (GP) to design
domain-specific and optimal kernel functions for
support vector classification (SVC) which automatically
adjusts the parameters. Complex low dimensional mapping
function is evolved using GP to construct an optimal
linear and Gaussian kernel functions in new feature
space. By using the principled kernel closure
properties, these basic kernels are then used to evolve
more optimal kernels. To evaluate the proposed method,
benchmark datasets from UCI are applied. The result
indicates that for some cases the proposed methods can
find a more optimal solution than evolving known
kernels.",
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keywords = "genetic algorithms, genetic programming, Gaussian
kernel functions, automatic parameter adjustment,
classification task, data mining application,
domain-specific kernel functions, feature space, kernel
evolution, low dimensional mapping function, optimal
kernel functions, optimal linear functions, principled
kernel closure properties, support vector
classification, support vector machines, Gaussian
processes, data mining, pattern classification, support
vector machines",
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DOI = "doi:10.1109/EAIS.2011.5945924",
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notes = "Also known as \cite{5945924}",
- }
Genetic Programming entries for
Mehrdad Alizadeh
Mohammad Mehdi Ebadzadeh
Citations