DocumentCode
3392339
Title
The quadratic property of the L-MBFGS methods for training neural networks
Author
Lin Zhao ; Dali Wang ; Yueting Yang
Author_Institution
Normal Sch., Beihua Univ., Jilin, China
fYear
2011
fDate
19-22 Aug. 2011
Firstpage
849
Lastpage
852
Abstract
In this paper, we introduce the use of limited memory modified BFGS method (L-MBFGS) to improve the efficiency of training algorithms for feedforward neural networks. The quadratic termination property of L-MBFGS algorithm is given which is an important quasi-Newton property.
Keywords
feedforward neural nets; learning (artificial intelligence); L-MBFGS method; feedforward neural networks; limited memory modified BFGS method; quadratic termination property; quasi-Newton property; training algorithms; Algorithm design and analysis; Biological neural networks; Feedforward neural networks; Mathematical model; Optimization; Training; backpropagation (BP); limited memory technique; neural networks; quasi-Newton methods; training algorithm;
fLanguage
English
Publisher
ieee
Conference_Titel
Mechatronic Science, Electric Engineering and Computer (MEC), 2011 International Conference on
Conference_Location
Jilin
Print_ISBN
978-1-61284-719-1
Type
conf
DOI
10.1109/MEC.2011.6025596
Filename
6025596
Link To Document