Title :
Pair attribute learning: network construction using pair features
Author :
Henderson, Eric K. ; Martinez, Tony R.
Author_Institution :
Cambridge Univ., UK
fDate :
6/24/1905 12:00:00 AM
Abstract :
We present the pair attribute learning (PAL) algorithm for the selection of relevant inputs and network topology. Correlations on training instance pairs are used to drive network construction of a single-hidden layer MLP. Results on nine learning problems demonstrate 70% less complexity, on average, without a significant loss of accuracy
Keywords :
learning (artificial intelligence); multilayer perceptrons; pattern classification; learning problems; network construction; network topology; pair attribute learning algorithm; pair features; single-hidden layer MLP; Accuracy; Application software; Backpropagation algorithms; Computer science; Iterative algorithms; Iterative methods; Network topology; Neural networks; Predictive maintenance; Training data;
Conference_Titel :
Neural Networks, 2002. IJCNN '02. Proceedings of the 2002 International Joint Conference on
Conference_Location :
Honolulu, HI
Print_ISBN :
0-7803-7278-6
DOI :
10.1109/IJCNN.2002.1007546
