Subnet weight modification algorithm for ensemble

Authors:
Jiang Meng;Kun An;Zhijie Wang
Affiliations:
College of Mechanical Engineering and Automatization, North University of China, Taiyuan, Shanxi, China;College of Information and Communication Engineering, North University of China, Taiyuan, Shanxi, China;Shanghai Institute of Technology, School of Mechanical and Automation Engineering, Shanghai, China
Venue:
ICIC'06 Proceedings of the 2006 international conference on Intelligent Computing - Volume Part I
Year:
2006

Citing 7
Cited 0

The Strength of Weak Learnability

Machine Learning
Boosting a weak learning algorithm by majority

Information and Computation
Bagging predictors

Machine Learning
A decision-theoretic generalization of on-line learning and an application to boosting

Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Ensembling neural networks: many could be better than all

Artificial Intelligence
Neural Network Ensembles

IEEE Transactions on Pattern Analysis and Machine Intelligence
Genetic algorithm based selective neural network ensemble

IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2

Quantified Score

Hi-index	0.00

Visualization

Abstract

In view of comparability between neural network ensemble and Adaline, a modification algorithm for ensemble weights is presented based on the gradient descent method. This algorithm can improve the generalization performance by modifying subnet weights after the ensemble subnets are trained individually. Simulation results indicate that the new algorithm is of subnet selection function similar to GASEN but on a different idea, and of better performance than single network, simple-averaged ensemble and GASEN.