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Pregled bibliografske jedinice broj: 575253

Recurrent sparse support vector regression machines trained by active learning in the time-domain

Čeperić, Vladimir; Gielen, Georges; Barić, Adrijan
Recurrent sparse support vector regression machines trained by active learning in the time-domain // Expert systems with applications, 39 (2012), 12; 10933-10942 doi:10.1016/j.eswa.2012.03.031 (međunarodna recenzija, članak, znanstveni)

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Naslov
Recurrent sparse support vector regression machines trained by active learning in the time-domain

Autori
Čeperić, Vladimir ; Gielen, Georges ; Barić, Adrijan

Izvornik
Expert systems with applications (0957-4174) 39 (2012), 12; 10933-10942

Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni

Ključne riječi
support vector machines; support vector regression; recurrent models; sparse models; active learning

Sažetak
A method for the sparse solution of recurrent support vector regression machines is presented. The proposed method achieves a high accuracy versus complexity and allows the user to adjust the complexity of the resulting model. The sparse representation is guaranteed by limiting the number of training data points for the support vector regression method. Each training data point is selected based on the accuracy of the fully recurrent model using the active learning principle applied to the successive time-domain data. The user can adjust the training time by selecting how often the hyper-parameters of the algorithm should be optimised. The advantages of the proposed method are illustrated on several examples, and the experiments clearly show that it is possible to reduce the number of support vectors and to significantly improve the accuracy versus complexity of recurrent support vector regression machines.

Izvorni jezik
Engleski

Znanstvena područja
Elektrotehnika, Računarstvo, Informacijske i komunikacijske znanosti



POVEZANOST RADA

Projekti:
036-0361621-1622 - Kvaliteta signala u integriranim sklopovima s mješovitim signalom (Barić, Adrijan, MZO ) ( CroRIS)

Ustanove:
Fakultet elektrotehnike i računarstva, Zagreb

Profili:

Avatar Url Adrijan Barić (autor)

Avatar Url Vladimir Čeperić (autor)

Poveznice na cjeloviti tekst rada:

doi ac.els-cdn.com www.sciencedirect.com dx.doi.org

Citiraj ovu publikaciju:

Čeperić, Vladimir; Gielen, Georges; Barić, Adrijan
Recurrent sparse support vector regression machines trained by active learning in the time-domain // Expert systems with applications, 39 (2012), 12; 10933-10942 doi:10.1016/j.eswa.2012.03.031 (međunarodna recenzija, članak, znanstveni)
Čeperić, V., Gielen, G. & Barić, A. (2012) Recurrent sparse support vector regression machines trained by active learning in the time-domain. Expert systems with applications, 39 (12), 10933-10942 doi:10.1016/j.eswa.2012.03.031.
@article{article, author = {\v{C}eperi\'{c}, Vladimir and Gielen, Georges and Bari\'{c}, Adrijan}, year = {2012}, pages = {10933-10942}, DOI = {10.1016/j.eswa.2012.03.031}, keywords = {support vector machines, support vector regression, recurrent models, sparse models, active learning}, journal = {Expert systems with applications}, doi = {10.1016/j.eswa.2012.03.031}, volume = {39}, number = {12}, issn = {0957-4174}, title = {Recurrent sparse support vector regression machines trained by active learning in the time-domain}, keyword = {support vector machines, support vector regression, recurrent models, sparse models, active learning} }
@article{article, author = {\v{C}eperi\'{c}, Vladimir and Gielen, Georges and Bari\'{c}, Adrijan}, year = {2012}, pages = {10933-10942}, DOI = {10.1016/j.eswa.2012.03.031}, keywords = {support vector machines, support vector regression, recurrent models, sparse models, active learning}, journal = {Expert systems with applications}, doi = {10.1016/j.eswa.2012.03.031}, volume = {39}, number = {12}, issn = {0957-4174}, title = {Recurrent sparse support vector regression machines trained by active learning in the time-domain}, keyword = {support vector machines, support vector regression, recurrent models, sparse models, active learning} }

Časopis indeksira:


  • Current Contents Connect (CCC)
  • Web of Science Core Collection (WoSCC)
    • Science Citation Index Expanded (SCI-EXP)
    • SCI-EXP, SSCI i/ili A&HCI
  • Scopus

Uključenost u ostale bibliografske baze podataka::


  • Cambridge/Computer and Information Abstracts
  • SCISEARCH
  • Scopus

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