DocumentCode :
2662378
Title :
Hybrid neural network and pattern classification learning algorithms
Author :
Kuh, Anthony ; Iseri, Gerald ; Mathur, Amit ; Huang, Zezhen
Author_Institution :
Dept. of Electr. Eng., Hawaii Univ., Manoa, Honolulu, HI, USA
fYear :
1990
fDate :
1-3 May 1990
Firstpage :
2512
Abstract :
Some key research issues in learning for feedforward networks are addressed. Some results from learning from examples are discussed, and how this relates to learning in networks is pointed out. Some limitations of algorithms and alternative strategies that involve changing network architectures or input data transformations are discussed. An example of how a self-organizing feature map can be used in conjunction with a feedforward network to achieve good results in isolated word recognition is given
Keywords :
computerised pattern recognition; learning systems; neural nets; speech recognition; alternative strategies; feedforward networks; hybrid neural networks; input data transformations; isolated word recognition; learning from examples; learning in networks; limitations of algorithms; network architectures; pattern classification learning algorithms; research issues; self-organizing feature map; Backpropagation algorithms; Biomedical signal processing; Character recognition; Classification algorithms; Iterative algorithms; Neural networks; Organizing; Pattern classification; Signal processing algorithms; Speech recognition;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Circuits and Systems, 1990., IEEE International Symposium on
Conference_Location :
New Orleans, LA
Type :
conf
DOI :
10.1109/ISCAS.1990.112521
Filename :
112521
Link To Document :
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