Comparison of Different Non-statistical Classification Methods

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62156489%3A43110%2F12%3A00190768" target="_blank" >RIV/62156489:43110/12:00190768 - 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

Comparison of Different Non-statistical Classification Methods

Popis výsledku v původním jazyce

In this article, we aim to compare different methods usable for solving classification problems. A substantial number of methods that are not based on mathematical statistics may be used. Exploring these methods is interesting, because they are often capable of solving problems, which are not easily solvable using classificators based purely on mathematical statistics. There are many approaches available such as support vector machines, neural networks, evolutionary algorithms, parallel coordinates, etc. In this article, we concentrate on describing different neural network approaches, parallel coordinates and genetic algorithms. Neural networks come in many flavors (e.g. multi-layer perceptron, non-linear autoregressive networks) and they have achieved some recognition. Genetic algorithms also have been used for classification many times before, but with mixed results. In this article, we describe and evaluate different capabilities of these methods when used for economic data. This f

Název v anglickém jazyce

Comparison of Different Non-statistical Classification Methods

Popis výsledku anglicky

In this article, we aim to compare different methods usable for solving classification problems. A substantial number of methods that are not based on mathematical statistics may be used. Exploring these methods is interesting, because they are often capable of solving problems, which are not easily solvable using classificators based purely on mathematical statistics. There are many approaches available such as support vector machines, neural networks, evolutionary algorithms, parallel coordinates, etc. In this article, we concentrate on describing different neural network approaches, parallel coordinates and genetic algorithms. Neural networks come in many flavors (e.g. multi-layer perceptron, non-linear autoregressive networks) and they have achieved some recognition. Genetic algorithms also have been used for classification many times before, but with mixed results. In this article, we describe and evaluate different capabilities of these methods when used for economic data. This f

Druh

D - Stať ve sborníku

CEP obor

IN - Informatika

OECD FORD obor

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Projekt

<a href="/cs/project/GAP403%2F11%2F2085" target="_blank" >GAP403/11/2085: Konstrukce metod pro vícefaktorové měření komplexní podnikové výkonnosti ve vybraném odvětví.</a><br>

Návaznosti

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

Rok uplatnění

2012

Kód důvěrnosti údajů

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

Název statě ve sborníku

Proceedings of the 30th International Conference Mathematical Methods in Economics 2012

ISBN

978-80-7248-779-0

ISSN

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e-ISSN

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Počet stran výsledku

6

Strana od-do

727-732

Název nakladatele

Silesian University in Opava, School of Business Administration in Karviná

Místo vydání

Karviná

Místo konání akce

Karviná

Datum konání akce

1. 1. 2012

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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