Memetic Evolutionary Learning for Local Unit Networks
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F10%3A00345155" target="_blank" >RIV/67985807:_____/10:00345155 - 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
Memetic Evolutionary Learning for Local Unit Networks
Original language description
In this work we propose two hybrid algorithms combining evolutionary search with optimization algorithms. One algorithm memetically combines global evolution with gradient descent local search, while the other is a two-step procedure combining linear optimization with evolutionary search. It is shown that these algorithms typically produce smaller local unit networks with performance similar to theoretically sound but large regularization networks.
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/GA201%2F08%2F1744" target="_blank" >GA201/08/1744: Complexity of perceptron and kernel networks</a><br>
Continuities
Z - Vyzkumny zamer (s odkazem do CEZ)
Others
Publication year
2010
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Advances in Neural Networks ? ISNN 2010
ISBN
978-3-642-13277-3
ISSN
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e-ISSN
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Number of pages
8
Pages from-to
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Publisher name
Springer
Place of publication
Berlin
Event location
Shanghai
Event date
Jun 6, 2010
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000279593300068