Using genetic programming to estimate performance of computational intelligence models
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F13%3A10286078" target="_blank" >RIV/00216208:11320/13:10286078 - isvavai.cz</a>
Result on the web
<a href="http://link.springer.com/chapter/10.1007%2F978-3-642-37213-1_18#page-1" target="_blank" >http://link.springer.com/chapter/10.1007%2F978-3-642-37213-1_18#page-1</a>
DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
Using genetic programming to estimate performance of computational intelligence models
Original language description
This paper deals with the problem of choosing the most suitable model for a new data mining task. The metric is proposed on the data mining tasks space, and similar tasks are identified based on this metric. A function estimating models performance on the new task from both the time and error point of view is evolved by means of genetic programming. The approach is verified on data containing results of several hundred thousands machine learning experiments.
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
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2013
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
ADAPTIVE AND NATURAL COMPUTING ALGORITHMS, ICANNGA 2013
ISBN
978-3-642-37213-1
ISSN
0302-9743
e-ISSN
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Number of pages
10
Pages from-to
169-178
Publisher name
SPRINGER-VERLAG
Place of publication
BERLIN, GERMANY
Event location
Lausanne; Switzerland
Event date
Apr 4, 2013
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000342815300018