id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
work_n25ntqmt35gq5ho2os4zbwvify	Ali Mehrizi	Robust Semi-Supervised Growing Self-Organizing Map	2018	11	.pdf	application/pdf	7424	1692	84	Semi-Supervised Growing Self Organizing Map (SSGSOM) is one of the best methods for online classification with partial labeled data. Furthermore, an adaptive method is proposed to adjust activation degree optimally to improve the performance of SSGSOM. The use of labeled data for learning together with clusering methods is called semi-supervised learning. They proposed a semi-supervised method based on a granular neural network to deal with data streams. Hsu and Halgamuge (2008) improved Fritzke method and preented a semi supervised algorithm based on GSOM. In our previous work, we proposed an online constraint semiupervised learning based on GSOM ( Allahyar, Yazdi, & Harati, • An adaptive online semi-supervised GSOM algorithm is proposed. Since the number of labeled data may be few, optimal operation of semi-supervised algorithms is very important. method proposed by Hsu is semi-supervised and online, it faces A semi-supervised extreme learning machine method based on co-training .	./cache/work_n25ntqmt35gq5ho2os4zbwvify.pdf	./txt/work_n25ntqmt35gq5ho2os4zbwvify.txt