An Adaptive Learning Algorithm for Supervised Neural Network with Contour Preserving Classification

Authors:
Piyabute Fuangkhon;Thitipong Tanprasert
Affiliations:
Distributed and Parallel Computing Research Laboratory Faculty of Science and Technology, Assumption University, Bangkok, Thailand;Distributed and Parallel Computing Research Laboratory Faculty of Science and Technology, Assumption University, Bangkok, Thailand
Venue:
AICI '09 Proceedings of the International Conference on Artificial Intelligence and Computational Intelligence
Year:
2009

Citing 5
Cited 1

Knowledge-based artificial neural networks

Artificial Intelligence
Extracting rules from neural networks by pruning and hidden-unit splitting

Neural Computation
Unified Integration of Explicit Knowledge and Learning by Example in Recurrent Networks

IEEE Transactions on Knowledge and Data Engineering
Integrating inductive neural network learning and explanation-based learning

IJCAI'93 Proceedings of the 13th international joint conference on Artifical intelligence - Volume 2
Learn++: an incremental learning algorithm for supervised neuralnetworks

IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews

A dual network adaptive learning algorithm for supervised neural network with contour preserving classification for soft real time applications

HAIS'10 Proceedings of the 5th international conference on Hybrid Artificial Intelligence Systems - Volume Part I

Quantified Score

Hi-index	0.01

Visualization

Abstract

A study of noise tolerance characteristics of an adaptive learning algorithm for supervised neural network is presented in this paper. The algorithm allows the existing knowledge to age out in slow rate as a supervised neural network is gradually retrained with consecutive sets of new samples, resembling the change of application locality under a consistent environment. The algorithm utilizes the contour preserving classification algorithm to pre-process the training data to improve the classification and the noise tolerance. The experimental results convincingly confirm the effectiveness of the algorithm and the improvement of noise tolerance.