Title :
Combining nodes in multiple neural network on large datasets
Author :
Kiatkaiwansiri, Ta ; Sinthupinyo, S.
Author_Institution :
Dept. of Comput. Eng., Chulalongkorn Univ., Bangkok, Thailand
Abstract :
Train a neural network with large dataset need a long training time. This paper presents a technique to reduce training time by divide a large dataset into n subsets and use those subsets to train multiple neural networks. In final, knowledge of trained networks are combined into a one network. The results from an experiment show that our technique has the same error like a network that trained by whole dataset but need less training time.
Keywords :
learning (artificial intelligence); neural nets; large datasets; multiple neural network; trained networks; Accuracy; Educational institutions; Knowledge engineering; Linear regression; Neural networks; Surface treatment; Training; Large datase; Linear Regression; Neural network;
Conference_Titel :
Digital Information and Communication Technology and it's Applications (DICTAP), 2014 Fourth International Conference on
Conference_Location :
Bangkok
Print_ISBN :
978-1-4799-3723-3
DOI :
10.1109/DICTAP.2014.6821651