Optimal predictive kernel regression via feature space principle components

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dc.contributor.author Fokoue, Ernest
dc.date.accessioned 2011-01-25T15:58:36Z
dc.date.available 2011-01-25T15:58:36Z
dc.date.issued 2011-01-01
dc.identifier.uri http://hdl.handle.net/1850/13134
dc.description.abstract We propose a simple use of principal component analysis in feature space that allows the derivation of optimal predictive kernel regression. The proposed approach is shown to perform well on both artificial and real data. Despite its incredible simplicity, the proposed method is found to compete very well with sophisticated statistical approaches like the Relevance Vector Machine and the Support Vector Machine. en_US
dc.language.iso en_US en_US
dc.relation RIT Scholars content from RIT Digital Media Library has moved from http://ritdml.rit.edu/handle/1850/13134 to RIT Scholar Works http://scholarworks.rit.edu/article/133, please update your feeds & links!
dc.subject Kernel regression en_US
dc.subject Principle component analysis en_US
dc.subject Sparsity en_US
dc.title Optimal predictive kernel regression via feature space principle components en_US
dc.type Article en_US

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