Complexity of Shallow Networks Representing Functions with Large Variations
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F14%3A00430374" target="_blank" >RIV/67985807:_____/14:00430374 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1007/978-3-319-11179-7_42" target="_blank" >http://dx.doi.org/10.1007/978-3-319-11179-7_42</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-319-11179-7_42" target="_blank" >10.1007/978-3-319-11179-7_42</a>
Alternative languages
Result language
angličtina
Original language name
Complexity of Shallow Networks Representing Functions with Large Variations
Original language description
Model complexities of networks representing multivariable functions is studied in terms of variational norms tailored to types of network units. It is shown that the size of the variational norm reflects both the number of hidden units and sizes of output weights. Lower bounds on growth of variational norms with increasing input dimension d are derived for Gaussian units and perceptrons. It is proven that variation of the d-dimensional parity with respect to Gaussian Support Vector Machines grows exponentially with d and for large values of d, almost any randomly-chosen Boolean function has variation with respect to perceptrons depending on d exponentially.
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/LD13002" target="_blank" >LD13002: Modeling of complex systems for softcomputing methods</a><br>
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2014
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Artificial Neural Networks and Machine Learning - ICANN 2014
ISBN
978-3-319-11178-0
ISSN
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e-ISSN
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Number of pages
8
Pages from-to
331-338
Publisher name
Springer
Place of publication
Cham
Event location
Hamburg
Event date
Sep 15, 2014
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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