Title :
Spectral partitioning for boundary estimation
Author_Institution :
Centre fof Vision, Speech & Signal Proc., Surrey Univ., Guildford, UK
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;
Conference_Titel :
Neural Networks, 1999. IJCNN '99. International Joint Conference on
Conference_Location :
Washington, DC
Print_ISBN :
0-7803-5529-6
DOI :
10.1109/IJCNN.1999.836058
