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

The result's identifiers

  • Result code in 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>

  • Result on the web

    <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>

Alternative languages

  • Result language

    angličtina

  • Original language name

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

  • Original language description

    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.

  • Czech name

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

  • OECD FORD branch

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

Result continuities

  • Project

  • Continuities

    S - Specificky vyzkum na vysokych skolach

Others

  • Publication year

    2018

  • Confidentiality

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

Data specific for result type

  • Article name in the collection

    Advances in intelligent systems and computing. Volume 565

  • ISBN

    978-3-319-60833-4

  • ISSN

    2194-5357

  • e-ISSN

    neuvedeno

  • Number of pages

    10

  • Pages from-to

    128-137

  • Publisher name

    Springer

  • Place of publication

    Berlin

  • Event location

    Marrákeš

  • Event date

    Nov 21, 2016

  • Type of event by nationality

    WRD - Celosvětová akce

  • UT code for WoS article