DocumentCode
2705448
Title
Optimal pruning of neural tree networks for improved generalization
Author
Sankar, Ananth ; Mammone, Richard J.
Author_Institution
Dept. of Electr. Eng., Rutgers Univ., Piscataway, NJ, USA
fYear
1991
fDate
8-14 Jul 1991
Firstpage
219
Abstract
An optimal pruning algorithm for neural tree networks (NTN) is presented. The NTN is grown by a constructive learning algorithm that decreases the classification error on the training data recursively. The optimal pruning algorithm is then used to improve generalization. The pruning algorithm is shown to be computationally inexpensive. Simulation results on a speaker-independent vowel recognition task are presented to show the improved generalization using the pruning algorithm
Keywords
neural nets; optimisation; speech recognition; trees (mathematics); classification error; constructive learning algorithm; generalization; neural tree networks; optimal pruning; speaker-independent vowel recognition; speech recognition; training data; Backpropagation algorithms; Binary trees; Classification algorithms; Classification tree analysis; Feedforward neural networks; Neural networks; Neurons; Speech recognition; Training data; Tree data structures;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks, 1991., IJCNN-91-Seattle International Joint Conference on
Conference_Location
Seattle, WA
Print_ISBN
0-7803-0164-1
Type
conf
DOI
10.1109/IJCNN.1991.155341
Filename
155341
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