Lenses Classification by means of Pseudo Neural Networks ? Two Approaches

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F70883521%3A28140%2F14%3A43871644" target="_blank" >RIV/70883521:28140/14:43871644 - 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

Lenses Classification by means of Pseudo Neural Networks ? Two Approaches

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. This structure is similar to classical artificial neural net therefore the name pseudo neural network isused. The proposed method for classifier structure synthesis utilizes Analytic Programming (AP) as the tool of the evolutionary symbolic regression. AP synthesizes a whole structure of the relation between inputs and output. 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 paper shows two approaches ? continues classification with one output node and classical approach with binary classification and more output nodes. Lenses data (one of benchmarks for classifiers) was used for testing of the proposed method. For experimentation, Differential Evolution for the main procedure and also fo

Název v anglickém jazyce

Lenses Classification by means of Pseudo Neural Networks ? Two Approaches

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. This structure is similar to classical artificial neural net therefore the name pseudo neural network isused. The proposed method for classifier structure synthesis utilizes Analytic Programming (AP) as the tool of the evolutionary symbolic regression. AP synthesizes a whole structure of the relation between inputs and output. 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 paper shows two approaches ? continues classification with one output node and classical approach with binary classification and more output nodes. Lenses data (one of benchmarks for classifiers) was used for testing of the proposed method. For experimentation, Differential Evolution for the main procedure and also fo

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

2014

Kód důvěrnosti údajů

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

Název statě ve sborníku

MENDEL 2014 20 th International conference on soft Computing

ISBN

978-80-214-4984-8

ISSN

1803-3814

e-ISSN

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Počet stran výsledku

6

Strana od-do

397-401

Název nakladatele

Vysoké učení technické v Brně, Fakulta strojního inženýrství

Místo vydání

Brno

Místo konání akce

Brno

Datum konání akce

25. 6. 2014

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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