Genetic complex multiple kernel for relevance vector regression
Created by W.Langdon from
gp-bibliography.bib Revision:1.8010
- @InProceedings{Wu:2010:ICACC,
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author = "Bing Wu and Wen-Qiong Zhang and Zhi-Wei Hu and
Jia-Hong Liang",
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title = "Genetic complex multiple kernel for relevance vector
regression",
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booktitle = "2nd International Conference on Advanced Computer
Control (ICACC 2010)",
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year = "2010",
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month = "27-29 " # mar,
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volume = "4",
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pages = "217--221",
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abstract = "Relevance vector machine (RVM) is a state-of-the-art
technique for regression and classification, as a
sparse Bayesian extension version of the support vector
machine. The selection of a kernel and associated
parameter is a critical step of RVM application. The
real-world application and recent researches have
emphasised the requirement to multiple kernel learning,
in order to boost the fitting accuracy by adapting
better the characteristics of the data. This paper
presents a data-driven evolutionary approach, called
Genetic Complex Multiple Kernel Relevance Vector
Regression (GCMK RVR), which combines genetic
programming(GP) and relevance vector regression to
evolve an optimal or near-optimal complex multiple
kernel function. Each GP chromosome is a tree that
encodes the mathematical expression of a complex
multiple kernel function. Numerical experiments on
several benchmark datasets show that the RVR involving
this GCMK perform better than not only the widely used
simple kernel, Polynomial, Gaussian RBF and Sigmoid
kernel, but also the convex linear multiple kernel
function.",
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keywords = "genetic algorithms, genetic programming,
classification method, data driven evolutionary
approach, genetic complex multiple kernel, genetic
complex multiple kernel relevance vector regression,
multiple kernel learning, relevance vector machine,
sparse Bayesian extension version, support vector
machine, Bayes methods, belief networks, learning
(artificial intelligence), numerical analysis, pattern
classification, regression analysis, support vector
machines",
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DOI = "doi:10.1109/ICACC.2010.5486939",
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notes = "Coll. of Mech. Eng. & Autom., Nat. Univ. of Defense
Technol., Changsha, China Also known as
\cite{5486939}",
- }
Genetic Programming entries for
Bing Wu
Wen-Qiong Zhang
Zhi-Wei Hu
Jia-Hong Liang
Citations