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

Kód výsledku v 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>

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

IN - Informatika

OECD FORD obor

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Projekt

<a href="/cs/project/GA201%2F08%2F1744" target="_blank" >GA201/08/1744: Složitost perceptronových a jádrových sítí</a><br>

Návaznosti

Z - Vyzkumny zamer (s odkazem do CEZ)

Rok uplatnění

2010

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Information Technologies - Applications and Theory

ISBN

978-80-970179-4-1

ISSN

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e-ISSN

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Počet stran výsledku

8

Strana od-do

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Název nakladatele

Pont

Místo vydání

Seňa

Místo konání akce

Smrekovica

Datum konání akce

21. 9. 2010

Typ akce podle státní příslušnosti

EUR - Evropská akce

Kód UT WoS článku

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