An Empirical Study of L-2-Boost with Echo State Networks

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27740%2F14%3A86096542" target="_blank" >RIV/61989100:27740/14:86096542 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1109/ISDA.2013.6920752" target="_blank" >http://dx.doi.org/10.1109/ISDA.2013.6920752</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/ISDA.2013.6920752" target="_blank" >10.1109/ISDA.2013.6920752</a>

Jazyk výsledku

angličtina

Název v původním jazyce

An Empirical Study of L-2-Boost with Echo State Networks

Popis výsledku v původním jazyce

At the beginning of the 2000s was introduced the Echo State Network model (ESN). The model has been successfully used in temporal learning tasks. In spite of its success in practical applications, the model can present some stability problems when the parameters are not well initialized. The stability of the model is associated with the spectrum of the weight matrix. To compute the spectra is an expensive tasks when the network is large. Below, the initialization of the network parameters can depend ofthe benchmark problem. In this paper, we investigate the performance of the L2-Boost procedure, one specific boosting technique, in time-series problems. We use an ensemble of ESNs which are randomly initialized without control of the spectral radius norm as weak predictors of the L2 procedure. Therefore, the procedure consists only in a random initialization of an ensemble of ESNs following of the L2-Boost steps.We evaluate this procedure on 5 widely used time-series benchmarks. Further

Název v anglickém jazyce

An Empirical Study of L-2-Boost with Echo State Networks

Popis výsledku anglicky

At the beginning of the 2000s was introduced the Echo State Network model (ESN). The model has been successfully used in temporal learning tasks. In spite of its success in practical applications, the model can present some stability problems when the parameters are not well initialized. The stability of the model is associated with the spectrum of the weight matrix. To compute the spectra is an expensive tasks when the network is large. Below, the initialization of the network parameters can depend ofthe benchmark problem. In this paper, we investigate the performance of the L2-Boost procedure, one specific boosting technique, in time-series problems. We use an ensemble of ESNs which are randomly initialized without control of the spectral radius norm as weak predictors of the L2 procedure. Therefore, the procedure consists only in a random initialization of an ensemble of ESNs following of the L2-Boost steps.We evaluate this procedure on 5 widely used time-series benchmarks. Further

Druh

D - Stať ve sborníku

CEP obor

IN - Informatika

OECD FORD obor

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Projekt

<a href="/cs/project/EE2.3.30.0055" target="_blank" >EE2.3.30.0055: Nové kreativní týmy v prioritách vědeckého bádání</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

International Conference on Intelligent Systems Design and Applications, ISDA

ISBN

978-1-4799-7938-7

ISSN

2164-7143

e-ISSN

—

Počet stran výsledku

6

Strana od-do

295-300

Název nakladatele

IEEE

Místo vydání

Los Alamitos

Místo konání akce

Salangor

Datum konání akce

8. 12. 2013

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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