Two Ways of using Artifiial Neural Networks in Knowledge Discovery from Chemical Materials Data

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F10%3A00348388" target="_blank" >RIV/67985807:_____/10:00348388 - isvavai.cz</a>
Result on the web
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DOI - Digital Object Identifier
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Result language
angličtina
Original language name
Two Ways of using Artifiial Neural Networks in Knowledge Discovery from Chemical Materials Data
Original language description
In the application area of chemical materials, data mining methods have been used for more than a decade. By far most popular have from the very beginning been methods based on artificial neural networks. However, they are frequently used without awareness of the difference between the numeric nature of knowledge obtained from data by neural network regression, and the symbolic nature of knowledge obtained by some other data mining methods. This paper explains that within the surrogate modelling approach, which plays an important role in this area, using numeric knowledge is justified. At the same time, it recalls the possibility to obtain symbolic knowledge from neural networks in the form of logical rules and describes a recently proposed method forthe extraction of Boolean rules in disjunctive normal form. Both ways of using neural networks are illustrated on examples from this application area.
Czech name
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Czech description
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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)

Publication year
2010
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
Information Technologies - Applications and Theory
ISBN
978-80-970179-4-1
ISSN
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e-ISSN
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Number of pages
8
Pages from-to
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Publisher name
Pont
Place of publication
Seňa
Event location
Smrekovica
Event date
Sep 21, 2010
Type of event by nationality
EUR - Evropská akce
UT code for WoS article
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