Learning from Data as an Optimization and Inverse Problem
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F12%3A00376629" target="_blank" >RIV/67985807:_____/12:00376629 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1007/978-3-642-27534-0_24" target="_blank" >http://dx.doi.org/10.1007/978-3-642-27534-0_24</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-642-27534-0_24" target="_blank" >10.1007/978-3-642-27534-0_24</a>
Alternative languages
Result language
angličtina
Original language name
Learning from Data as an Optimization and Inverse Problem
Original language description
Learning form data is investigated as minimization of empirical error functional in spaces of continuous functions and spaces defined by kernels. Using methods from theory of inverse problems, an alternative proof of Representer Theorem is given. Regularized and non regularized minimization of empirical error is compared.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
IN - Informatics
OECD FORD branch
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Result continuities
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)
Others
Publication year
2012
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Computational Intelligence
ISBN
978-3-642-27533-3
ISSN
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e-ISSN
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Number of pages
12
Pages from-to
361-372
Publisher name
Springer
Place of publication
Heidelberg
Event location
Valencia
Event date
Oct 24, 2010
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000309733800024