Title :
Optimal, matching-score network for pattern classification
Author :
Luk, Andrew ; Leung, Wai-Fung
Author_Institution :
Dept. of Electron. Eng., City Polytech. of Hong Kong, Kowloon, Hong Kong
Abstract :
This paper presents a design method for an optimal matching-score (MS) network with exponential and sigmoid activation functions. By using a new orthogonal learning process, the proposed net is capable of learning new patterns by growing its hidden layer and without recomputing the entire interconnection weight matrices. Simulation results on signal classification and character recognition show that the MS net is highly robust to noise. Besides, the generalization capability of the net is shown superior than that of the backpropagation net
Keywords :
feedforward neural nets; learning (artificial intelligence); matrix inversion; multilayer perceptrons; pattern classification; transfer functions; character recognition; exponential activation functions; hidden layer; matrix inversion method; noise robustness; optimal matching-score network; orthogonal learning process; pattern classification; sigmoid activation functions; signal classification; simulation results; two-layer feedforward matching-score net; Backpropagation algorithms; Cities and towns; Design engineering; Design methodology; Multi-layer neural network; Neurons; Optimal matching; Pattern classification; Pattern matching; Symmetric matrices;
Conference_Titel :
Acoustics, Speech, and Signal Processing, 1994. ICASSP-94., 1994 IEEE International Conference on
Conference_Location :
Adelaide, SA
Print_ISBN :
0-7803-1775-0
DOI :
10.1109/ICASSP.1994.389585
