DocumentCode
2142903
Title
Parsimonious side propagation
Author
Bradley, P.S. ; Mangasarian, O.L.
Author_Institution
Dept. of Comput. Sci., Wisconsin Univ., Madison, WI, USA
Volume
3
fYear
1998
fDate
12-15 May 1998
Firstpage
1873
Abstract
A fast parsimonious linear-programming-based algorithm for training neural networks is proposed that suppresses redundant features while using a minimal tra number of hidden units. This is achieved by propagating sideways to newly added hidden units the task of separating successive groups of unclassified points. Computational results show an improvement of 26.53% and 19.76% in tenfold cross-validation test correctness over a parsimonious perceptron on two publicly available datasets
Keywords
learning (artificial intelligence); linear programming; neural nets; algorithm; cross-validation test correctness; datasets; fast parsimonious linear-programming; neural network training; parsimonious perceptron; parsimonious side propagation; unclassified points separation; Computer networks; Mathematical programming; Neural networks; Particle separators; Testing; Training data; Writing;
fLanguage
English
Publisher
ieee
Conference_Titel
Acoustics, Speech and Signal Processing, 1998. Proceedings of the 1998 IEEE International Conference on
Conference_Location
Seattle, WA
ISSN
1520-6149
Print_ISBN
0-7803-4428-6
Type
conf
DOI
10.1109/ICASSP.1998.681829
Filename
681829
Link To Document