Title :
Improving discriminant neural network (DNN) design by the use of principal component analysis
Author :
Li, Qi ; Tufts, Donald W.
Author_Institution :
Dept. of Electr. Eng., Rhode Island Univ., Kingston, RI, USA
Abstract :
Investigations of the design of a discriminant neural network (DNN) have shown the advantages of sequential design of hidden nodes and pruning of the training data for improved classification and fast training time. The performance can be further improved by adding the capability of a nonlinear, principal component discriminant node. This type of hidden node is useful for separating classes which have common mean vectors and are overlapped on one other
Keywords :
learning (artificial intelligence); neural net architecture; classification; discriminant neural network design; fast training time; hidden node; hidden nodes; mean vectors; neural network architecture; nonlinear principal component discriminant node; performance; principal component analysis; sequential design; training data pruning; Analytical models; Backpropagation; Covariance matrix; Design methodology; Eigenvalues and eigenfunctions; Linear discriminant analysis; Neural networks; Principal component analysis; Training data; Vectors;
Conference_Titel :
Acoustics, Speech, and Signal Processing, 1995. ICASSP-95., 1995 International Conference on
Conference_Location :
Detroit, MI
Print_ISBN :
0-7803-2431-5
DOI :
10.1109/ICASSP.1995.479709
