Cost Functions Based on Different Types of Distance Measurements for Pseudo Neural Network Synthesis

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F70883521%3A28140%2F15%3A43873865" target="_blank" >RIV/70883521:28140/15:43873865 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1007/978-3-319-19824-8_24" target="_blank" >http://dx.doi.org/10.1007/978-3-319-19824-8_24</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-319-19824-8_24" target="_blank" >10.1007/978-3-319-19824-8_24</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Cost Functions Based on Different Types of Distance Measurements for Pseudo Neural Network Synthesis

Popis výsledku v původním jazyce

This research deals with a novel approach to classification. New classifiers are synthesized as a complex structure via evolutionary symbolic computation techniques. Compared to previous research, this paper synthesizes multi-input-multi-output (MIMO) classifiers with different cost function based on distance measurements. An inspiration for this work came from the field of artificial neural networks (ANN). The proposed technique creates a relation between inputs and outputs as a whole structure together with numerical values which could be observed as weights in ANN. Distances used in cost functions were: Manhattan (absolute distances of output vectors), Euclidean, Chebyshev (maximum distance value), Canberra distance, Bray - Curtis. The Analytic Programming (AP) was utilized as the tool of synthesis by means of the evolutionary symbolic regression. For experimentation, Differential Evolution for the main procedure and also for meta-evolution version of analytic programming was used.

Název v anglickém jazyce

Cost Functions Based on Different Types of Distance Measurements for Pseudo Neural Network Synthesis

Popis výsledku anglicky

This research deals with a novel approach to classification. New classifiers are synthesized as a complex structure via evolutionary symbolic computation techniques. Compared to previous research, this paper synthesizes multi-input-multi-output (MIMO) classifiers with different cost function based on distance measurements. An inspiration for this work came from the field of artificial neural networks (ANN). The proposed technique creates a relation between inputs and outputs as a whole structure together with numerical values which could be observed as weights in ANN. Distances used in cost functions were: Manhattan (absolute distances of output vectors), Euclidean, Chebyshev (maximum distance value), Canberra distance, Bray - Curtis. The Analytic Programming (AP) was utilized as the tool of synthesis by means of the evolutionary symbolic regression. 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

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

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

Rok uplatnění

2015

Kód důvěrnosti údajů

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

Název statě ve sborníku

Advances in Intelligent Systems and computing. Mendel 2015

ISBN

978-3-319-19823-1

ISSN

2194-5357

e-ISSN

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Počet stran výsledku

11

Strana od-do

291-301

Název nakladatele

VUT v Brně

Místo vydání

Brno

Místo konání akce

Brno

Datum konání akce

23. 6. 2015

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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