THE S2-ENSEMBLE FUSION ALGORITHM

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27740%2F11%3A86084426" target="_blank" >RIV/61989100:27740/11:86084426 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1142/S0129065711003012" target="_blank" >http://dx.doi.org/10.1142/S0129065711003012</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1142/S0129065711003012" target="_blank" >10.1142/S0129065711003012</a>

Jazyk výsledku

angličtina

Název v původním jazyce

THE S2-ENSEMBLE FUSION ALGORITHM

Popis výsledku v původním jazyce

This paper presents a novel model for performing classification and visualization of high-dimensional data by means of combining two enhancing techniques. The first is a semi-supervised learning, an extension of the supervised learning used to incorporate unlabeled information to the learning process. The second is an ensemble learning to replicate the analysis performed, followed by a fusion mechanism that yields as a combined result of previously performed analysis in order to improve the result of asingle model. The proposed learning schema, termed S2-Ensemble, is applied to several unsupervised learning algorithms within the family of topology maps, such as the Self-Organizing Maps and the Neural Gas. This study also includes a thorough research of the characteristics of these novel schemes, by means quality measures, which allow a complete analysis of the resultant classifiers from the viewpoint of various perspectives over the different ways that these classifiers are used. The

Název v anglickém jazyce

THE S2-ENSEMBLE FUSION ALGORITHM

Popis výsledku anglicky

This paper presents a novel model for performing classification and visualization of high-dimensional data by means of combining two enhancing techniques. The first is a semi-supervised learning, an extension of the supervised learning used to incorporate unlabeled information to the learning process. The second is an ensemble learning to replicate the analysis performed, followed by a fusion mechanism that yields as a combined result of previously performed analysis in order to improve the result of asingle model. The proposed learning schema, termed S2-Ensemble, is applied to several unsupervised learning algorithms within the family of topology maps, such as the Self-Organizing Maps and the Neural Gas. This study also includes a thorough research of the characteristics of these novel schemes, by means quality measures, which allow a complete analysis of the resultant classifiers from the viewpoint of various perspectives over the different ways that these classifiers are used. The

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

IN - Informatika

OECD FORD obor

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Projekt

<a href="/cs/project/ED1.1.00%2F02.0070" target="_blank" >ED1.1.00/02.0070: Centrum excelence IT4Innovations</a><br>

Návaznosti

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

Rok uplatnění

2011

Kód důvěrnosti údajů

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

Název periodika

International Journal of Neural Systems

ISSN

0129-0657

e-ISSN

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Svazek periodika

21

Číslo periodika v rámci svazku

6

Stát vydavatele periodika

SG - Singapurská republika

Počet stran výsledku

20

Strana od-do

505-525

Kód UT WoS článku

000297557900006

EID výsledku v databázi Scopus

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