Evolutionary Learning of Regularization Networks with Multi-kernel Units

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F11%3A00369159" target="_blank" >RIV/67985807:_____/11:00369159 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1007/978-3-642-21105-8_62" target="_blank" >http://dx.doi.org/10.1007/978-3-642-21105-8_62</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-642-21105-8_62" target="_blank" >10.1007/978-3-642-21105-8_62</a>

Result language
angličtina
Original language name
Evolutionary Learning of Regularization Networks with Multi-kernel Units
Original language description
Regularization networks represent an important supervised learning method applicable for regression and classification tasks. They benefit from very good theoretical background, although the presence of meta parameters is their drawback. The meta parameters, including the type of kernel function, are typically supposed to be given in advance and come ready as an input of the algorithm. In this paper, we propose multi-kernel functions, namely product kernel functions and composite kernel functions. The choice of kernel function becomes part of the optimization process, for which a new evolutionary learning algorithm is introduced that deals with different kernel functions, including composite kernels. The results are demonstrated on experiments with benchmark tasks.
Czech name
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Czech description
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Project
<a href="/en/project/GAP202%2F11%2F1368" target="_blank" >GAP202/11/1368: Learning of functional relationships from high-dimensional data</a><br>
Continuities
Z - Vyzkumny zamer (s odkazem do CEZ)

Publication year
2011
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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