Estimates of Model Complexity in Neural-Network Learning
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F09%3A00328492" target="_blank" >RIV/67985807:_____/09:00328492 - isvavai.cz</a>
Result on the web
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DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
Estimates of Model Complexity in Neural-Network Learning
Original language description
Model complexity in neural-network learning is investigated using tools from nonlinear approximation and integration theory. Estimates of network complexity are obtained from inspection of upper bounds on convergence of minima of error functionals over networks with an increasing number of units to their global minima. The estimates are derived using integral transforms induced by computational units. The role of dimensionality of training data defining error functionals is discussed.
Czech name
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Czech description
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Classification
Type
C - Chapter in a specialist book
CEP classification
IN - Informatics
OECD FORD branch
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Result continuities
Project
<a href="/en/project/1M0567" target="_blank" >1M0567: Centre for Applied Cybernetics</a><br>
Continuities
Z - Vyzkumny zamer (s odkazem do CEZ)
Others
Publication year
2009
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Book/collection name
Innovations in Neural Information Paradigms and Applications
ISBN
978-3-642-04002-3
Number of pages of the result
15
Pages from-to
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Number of pages of the book
294
Publisher name
Springer
Place of publication
Berlin
UT code for WoS chapter
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