Some Comparisons of Radial and Kernel Computational Models

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F11%3A00366051" target="_blank" >RIV/67985807:_____/11:00366051 - isvavai.cz</a>
Result on the web
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DOI - Digital Object Identifier
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Result language
angličtina
Original language name
Some Comparisons of Radial and Kernel Computational Models
Original language description
Mathematical properties of two types of computational models popular in neurocomputing, radial-basis function networks (RBF) and kernel models, are compared. Both models have their advantages: RBF networks are known to be universal approximators and theyallow higher flexibility in choice of free parameters which leads to smaller model complexity. On the other hand, kernel models benefit from geometrical properties of Hilbert spaces generated by symmetric positive semidefinite kernels. These propertiesallow applications of maximal margin classification, regularization modeling generalization in learning from data and description of optimal solutions of learning tasks. We investigate these two types of models in the framework of kernel units with fixedand variable widths. We give conditions on kernels with fixed widths implying universal approximation property and describe behavior of kernel stabilizers with changing widths and input dimensions. We illustrate our results by the exampl
Czech name
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Czech description
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Project
<a href="/en/project/1M0567" target="_blank" >1M0567: Centre for Applied Cybernetics</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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