Improved adaptive splitting and selection: the hybrid training method of a classifier based on a feature space partitioning

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27740%2F14%3A86087620" target="_blank" >RIV/61989100:27740/14:86087620 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1142/S0129065714300071" target="_blank" >http://dx.doi.org/10.1142/S0129065714300071</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1142/S0129065714300071" target="_blank" >10.1142/S0129065714300071</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Improved adaptive splitting and selection: the hybrid training method of a classifier based on a feature space partitioning

Popis výsledku v původním jazyce

Currently, methods of combined classification are the focus of intense research. A properly designed group of combined classifiers exploiting knowledge gathered in a pool of elementary classifiers can successfully outperform a single classifier. There are two essential issues to consider when creating combined classifiers: how to establish the most comprehensive pool and how to design a fusion model that allows for taking full advantage of the collected knowledge. In this work, we address the issues andpropose an AdaSS+, training algorithm dedicated for the compound classifier system that effectively exploits local specialization of the elementary classifiers. An effective training procedure consists of two phases. The first phase detects the classifier competencies and adjusts the respective fusion parameters. The second phase boosts classification accuracy by elevating the degree of local specialization. The quality of the proposed algorithms are evaluated on the basis of a wide ran

Název v anglickém jazyce

Improved adaptive splitting and selection: the hybrid training method of a classifier based on a feature space partitioning

Popis výsledku anglicky

Currently, methods of combined classification are the focus of intense research. A properly designed group of combined classifiers exploiting knowledge gathered in a pool of elementary classifiers can successfully outperform a single classifier. There are two essential issues to consider when creating combined classifiers: how to establish the most comprehensive pool and how to design a fusion model that allows for taking full advantage of the collected knowledge. In this work, we address the issues andpropose an AdaSS+, training algorithm dedicated for the compound classifier system that effectively exploits local specialization of the elementary classifiers. An effective training procedure consists of two phases. The first phase detects the classifier competencies and adjusts the respective fusion parameters. The second phase boosts classification accuracy by elevating the degree of local specialization. The quality of the proposed algorithms are evaluated on the basis of a wide ran

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/EE2.3.30.0016" target="_blank" >EE2.3.30.0016: Příležitost pro mladé výzkumníky</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 periodika

International Journal of Neural Systems

ISSN

0129-0657

e-ISSN

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Svazek periodika

24

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

SG - Singapurská republika

Počet stran výsledku

18

Strana od-do

1-18

Kód UT WoS článku

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EID výsledku v databázi Scopus

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