Technical Program

Paper Detail

Paper:MLSP-P7.6
Session:Pattern Recognition and Classification II
Time:Friday, May 21, 15:30 - 17:30
Presentation: Poster
Topic: Machine Learning for Signal Processing: Signal detection, Pattern Recognition and Classification
Title: GAS IDENTIFICATION WITH MICROELECTRONIC GAS SENSOR IN PRESENCE OF DRIFT USING ROBUST GMM
Authors: Sofiane Brahim-Belhouari; Hong Kong University of Science and Technology 
 Amine Bermak; Hong Kong University of Science and Technology 
 Philip C. H. Chan; Hong Kong University of Science and Technology 
Abstract: The pattern recognition problem for real life applications of gasidentification is particularly challenging due to the small amountof data available and the temporal variability of the instrumentmainly caused by drift. In this paper we present a gasidentification approach based on class-conditional densityestimation using Gaussian mixture models (GMM). A driftcounteraction approach based on extracting robust feature using asimulated drift is proposed. The performance of the retrained GMMshows the effectiveness of the new approach in improving theclassification performance in the presence of artificial drift.
 
           Back


Home -||- Organizing Committee -||- Technical Committee -||- Technical Program -||- Plenaries
Paper Submission -||- Special Sessions -||- ITT -||- Paper Review -||- Exhibits -||- Tutorials
Information -||- Registration -||- Travel Insurance -||- Housing -||- Workshops

©2015 Conference Management Services, Inc. -||- email: webmaster@icassp2004.org -||- Last updated Wednesday, April 07, 2004