Kernel Function Tuning for Single-Layer Neural Networks

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F18%3A00493061" target="_blank" >RIV/67985807:_____/18:00493061 - isvavai.cz</a>
Result on the web
<a href="http://www.ijmlc.org/index.php?m=content&c=index&a=show&catid=79&id=831" target="_blank" >http://www.ijmlc.org/index.php?m=content&c=index&a=show&catid=79&id=831</a>
DOI - Digital Object Identifier
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Result language
angličtina
Original language name
Kernel Function Tuning for Single-Layer Neural Networks
Original language description
This paper describes an unified learning framework for kernel networks with one hidden layer, including models like radial basis function networks and regularization networks. The learning procedure consists of meta-parameter tuning wrapping the standard parameter optimization part. Several variants of learning are described and tested on various classification and regression problems. It is shown that meta-learning can improve the performance of models for the price of higher time complexity. © 2018, International Association of Computer Science and Information Technology.
Czech name
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Czech description
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Type
J<sub>SC</sub> - Article in a specialist periodical, which is included in the SCOPUS database
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project
<a href="/en/project/GA15-18108S" target="_blank" >GA15-18108S: Model complexity of neural, radial, and kernel networks</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2018
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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