Improving the speed and quality of extreme learning machine by conjugate gradient method

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F18%3A10241695" target="_blank" >RIV/61989100:27240/18:10241695 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1007/978-3-319-60834-1_14" target="_blank" >http://dx.doi.org/10.1007/978-3-319-60834-1_14</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-319-60834-1_14" target="_blank" >10.1007/978-3-319-60834-1_14</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Improving the speed and quality of extreme learning machine by conjugate gradient method

Popis výsledku v původním jazyce

Extreme Learning Machine (ELM) is a novel learning algorithm. It is basically a feedforward neural network with one hidden layer, fixed input weights and fixed biases. ELM has become popular in recent years due to the fast learning speed and good generalization performance. A novel approach based on Conjugate Gradient Method (CG) is proposed in this Article to improve original ELM. As experiments show, proposed approach is both faster and have higher quality on all tested datasets. Results have also shown that higher quality can be achieved after four iterations of CG. This means that expensive pseudo-inverse operation used in the original algorithm can be replaced by four matrix-vector multiplication and several scalar products. Therefore the proposed approach is more suitable for parallel architectures or can be used for larger datasets. (C) 2018, Springer International Publishing AG.

Název v anglickém jazyce

Improving the speed and quality of extreme learning machine by conjugate gradient method

Popis výsledku anglicky

Extreme Learning Machine (ELM) is a novel learning algorithm. It is basically a feedforward neural network with one hidden layer, fixed input weights and fixed biases. ELM has become popular in recent years due to the fast learning speed and good generalization performance. A novel approach based on Conjugate Gradient Method (CG) is proposed in this Article to improve original ELM. As experiments show, proposed approach is both faster and have higher quality on all tested datasets. Results have also shown that higher quality can be achieved after four iterations of CG. This means that expensive pseudo-inverse operation used in the original algorithm can be replaced by four matrix-vector multiplication and several scalar products. Therefore the proposed approach is more suitable for parallel architectures or can be used for larger datasets. (C) 2018, Springer International Publishing AG.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2018

Kód důvěrnosti údajů

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

Název statě ve sborníku

Advances in intelligent systems and computing. Volume 565

ISBN

978-3-319-60833-4

ISSN

2194-5357

e-ISSN

neuvedeno

Počet stran výsledku

10

Strana od-do

128-137

Název nakladatele

Springer

Místo vydání

Berlin

Místo konání akce

Marrákeš

Datum konání akce

21. 11. 2016

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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