TUTCRIS - Tampereen teknillinen yliopisto

TUTCRIS

Exploiting local class information in extreme learning machine

Tutkimustuotosvertaisarvioitu

Yksityiskohdat

AlkuperäiskieliEnglanti
OtsikkoNCTA 2014 - Proceedings of the International Conference on Neural Computation Theory and Applications
KustantajaINSTICC PRESS
Sivut49-55
Sivumäärä7
ISBN (painettu)9789897580543
TilaJulkaistu - 2014
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisussa
Tapahtuma6th International Conference on Neural Computation Theory and Applications, NCTA 2014, Part of the 6th International Joint Conference on Computational Intelligence, IJCCI 2014 - Rome, Italia
Kesto: 22 lokakuuta 201424 lokakuuta 2014

Conference

Conference6th International Conference on Neural Computation Theory and Applications, NCTA 2014, Part of the 6th International Joint Conference on Computational Intelligence, IJCCI 2014
MaaItalia
KaupunkiRome
Ajanjakso22/10/1424/10/14

Tiivistelmä

In this paper we propose an algorithm for Single-hidden Layer Feedforward Neural networks training. Based on the observation that the learning process of such networks can be considered to be a non-linear mapping of the training data to a high-dimensional feature space, followed by a data projection process to a low-dimensional space where classification is performed by a linear classifier, we extend the Extreme Learning Machine (ELM) algorithm in order to exploit the local class information in its optimization process. The proposed Local Class Variance Extreme Learning Machine classifier is evaluated in facial image classification problems, where we compare its performance with that of other ELM-based classifiers. Experimental results show that the incorporation of local class information in the ELM optimization process enhances classification performance.

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