DocumentCode
1910829
Title
Spectral partitioning for boundary estimation
Author
Windeatt, Terry
Author_Institution
Centre fof Vision, Speech & Signal Proc., Surrey Univ., Guildford, UK
Volume
5
fYear
1999
fDate
1999
Firstpage
3102
Abstract
We propose a spectral technique for analysing intermediate feature space of multiple classifier decisions, which enables a separable subset of patterns to be extracted. The method is applied to finding a set of patterns that are inconsistently classified, a random subset of which is left out of the training set of each expert in a multiple classifier framework
Keywords
multilayer perceptrons; pattern classification; boundary estimation; intermediate feature space analysis; multiple classifier decisions; multiple classifier framework; separable subset extraction; spectral partitioning; Bagging; Boosting; Classification tree analysis; Filtering; Pattern recognition; Signal analysis; Spectral analysis; Speech analysis; Training data; Voting;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks, 1999. IJCNN '99. International Joint Conference on
Conference_Location
Washington, DC
ISSN
1098-7576
Print_ISBN
0-7803-5529-6
Type
conf
DOI
10.1109/IJCNN.1999.836058
Filename
836058
Link To Document