Issue Date: 6-7 March 2010rnrntOn page(s): rnt52rnttrn- 55rnrnrnLocation: Wuhan, ChinarnrnPrint ISBN: 978-1-4244-6388-6rnrnrnrnttrnDigital Object Identifier: href='http://dx.doi.org/10.1109/ETCS.2010.154' target='_blank'>10.1109/ETCS.2010.154 rnrnDate of Current Version: trnrnt2010-05-06 14:33:52.0rnrnt rntt class="body-text">rntname="Abstract">>Abstractrn>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 kernel function and parameter selection is a key problem in the research of RVM. The real-world application and recent researches have emphasized the requirement to multiple kernel learning. This paper prop;
Genetic Multiple Kernel; Relevance vector regression; genetic programming;
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