Principal-feature classification

Author

Tufts, Donald W. ; Li, Qi

Author_Institution

Dept. of Electr. & Comput. Eng., Rhode Island Univ., Kingston, RI, USA

fYear

1995

fDate

31 Aug-2 Sep 1995

Firstpage

125

Lastpage

134

Abstract

The concept of classification using principal features is presented. The principal features defined in this paper are analogous to principal components in statistics and linear algebra. Neural network training can be done by sequential identification of principal features and corresponding pruning of the training data. Two neural network simplification algorithms, lossless and lossy simplifications, make the the classifier design more efficient. The design procedure is compared with other classifier design algorithms

Keywords

learning (artificial intelligence); linear algebra; neural nets; pattern classification; signal detection; statistical analysis; linear algebra; lossless simplification; lossy simplifications; neural network; principal-feature network; sequential identification; signal recognition; statistics; Algebra; Algorithm design and analysis; Backpropagation algorithms; Neural networks; Performance analysis; Principal component analysis; Statistics; Testing; Training data; Vectors;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks for Signal Processing [1995] V. Proceedings of the 1995 IEEE Workshop

Conference_Location

Cambridge, MA

Print_ISBN

0-7803-2739-X

Type

conf

DOI

10.1109/NNSP.1995.514886

Filename

514886

Link To Document

https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=2363180