Evolutionary Synthesis of Complex Structures - Pseudo Neural Networks for the Task of Iris Dataset Classification

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F70883521%3A28140%2F13%3A43869796" target="_blank" >RIV/70883521:28140/13:43869796 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1007/978-3-319-00542-3_22" target="_blank" >http://dx.doi.org/10.1007/978-3-319-00542-3_22</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-319-00542-3_22" target="_blank" >10.1007/978-3-319-00542-3_22</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Evolutionary Synthesis of Complex Structures - Pseudo Neural Networks for the Task of Iris Dataset Classification

Popis výsledku v původním jazyce

This research deals with a novel approach to classification. This paper deals with a synthesis of a complex structure which serves as a classifier. Classical artificial neural networks, where a relation between inputs and outputs is based on the mathematical transfer functions and optimized numerical weights, were an inspiration for this work. The proposed method utilizes Analytic Programming (AP) as the tool of the evolutionary symbolic regression. AP synthesizes a whole structure of the relation between inputs and output. Iris data (a known benchmark for classifiers) was used for testing of the proposed method. For experimentation, Differential Evolution for the main procedure and also for meta-evolution version of analytic programming was used.

Název v anglickém jazyce

Evolutionary Synthesis of Complex Structures - Pseudo Neural Networks for the Task of Iris Dataset Classification

Popis výsledku anglicky

This research deals with a novel approach to classification. This paper deals with a synthesis of a complex structure which serves as a classifier. Classical artificial neural networks, where a relation between inputs and outputs is based on the mathematical transfer functions and optimized numerical weights, were an inspiration for this work. The proposed method utilizes Analytic Programming (AP) as the tool of the evolutionary symbolic regression. AP synthesizes a whole structure of the relation between inputs and output. Iris data (a known benchmark for classifiers) was used for testing of the proposed method. For experimentation, Differential Evolution for the main procedure and also for meta-evolution version of analytic programming was used.

Druh

D - Stať ve sborníku

CEP obor

IN - Informatika

OECD FORD obor

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Projekt

<a href="/cs/project/ED2.1.00%2F03.0089" target="_blank" >ED2.1.00/03.0089: Centrum bezpečnostních, informačních a pokročilých technologií (CEBIA-Tech)</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2013

Kód důvěrnosti údajů

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

Název statě ve sborníku

Nostradamus 2013: Prediction, Modeling and Analysis of Complex Systems

ISBN

978-3-319-00541-6

ISSN

2194-5357

e-ISSN

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Počet stran výsledku

10

Strana od-do

211-220

Název nakladatele

Springer-Verlag Berlin

Místo vydání

Heidelberg

Místo konání akce

Ostrava

Datum konání akce

3. 6. 2013

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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