Mendeley
TY  _  JOUR
ID  -  139604202225116863
TI  -  Semi-Supervised Learning Based on Extreme Learning
JO  -  Nashriyyah -i Muhandisi -i Barq va Muhandisi -i Kampyutar -i Iran
JA  -  ES
LA  -  fa
SN  -  16823745
AU  -  Mehrizi A.
AU  -  Sadoghi Yazdi Hadi
AU  -  Mahdavi Chabok Seyed Javad 
AD  -  دانشگاه آزاد اسلامی، واحد مشهد
AD  -  Ferdowsi University of Mashhad
AD  -  Department of Computer Engineering, Mashhad Branch, Islamic Azad University, Mashhad, IRAN
Y1  -  2020
PY  -  2020
VL  -  4
IS  -  
SP  -  112
EP  -  122
KW  -  Semi-supervised learning
GSOM
extreme learning
online learning
DO  -  
N2  -  Semi-supervised learning with growing self-organizing map (GSOM) is used in many applications, such as clustering. The main challenges in the Semi-supervised GSOM are calculating parameters such as shape and structure of clustering layer, activation level, and weights of classifier layer. Current approaches use initiative methods with a local look have trying to determine these parameters; which its effect, the results of these algorithms is highly dependent on the conditions. This paper studies a semi-supervised learning method based on GSOM and extreme learning for the first time. The proposed method, without the direct calculation of the GSOM parameters and using the extreme learning determines label of each data. Error resulted from the feedback system is used to optimize extreme learning and GSOM. In this paper, in addition to investigating the convergence analysis of the proposed method, sequential extreme learning is also provided for semi-supervised GSOM. Experiments conducted on online and partially labeled data show that the proposed method has a relative advantage in terms of accuracy on semi-supervised GSOM.
UR  - rimag.ir/en/Article/28146
L1  - rimag.ir/en/Article/Download/28146
ER  -  
