Iterative Hard Thresholding Based Extreme Learning Machine
Küçük Resim Yok
Tarih
2015
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Ieee
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Extreme Learning Machines (ELM) is a new learning algorithm for Single hidden Layer Feed-forward Networks (SLFNs). The ELM has better generalization, rapid training and lower complexity, however, the method suffer from singularity problem and obtaining optimum number of neurons in the hidden layer. In this paper, we considered an IHT for sparse approximation of the output weights vector of the ELM network. The performance evaluation of the proposed method which is called IHT-ELM, was chosen out on four commonly used medical dataset for prediction purposes. The results showed that IHT-ELM has several advantages against the original ELM methods such as obtaining optimum number of neurons and low complexity.
Açıklama
23nd Signal Processing and Communications Applications Conference (SIU) -- MAY 16-19, 2015 -- Inonu Univ, Malatya, TURKEY
Anahtar Kelimeler
Extreme learning machine, single-hidden-layer feed forward neural networks, sparsity, iterative hard thresholding
Kaynak
2015 23rd Signal Processing and Communications Applications Conference (Siu)
WoS Q Değeri
N/A
Scopus Q Değeri
N/A
