Minimum Classification Error Using Time-Frequency Analysis

Breakenridge, C. and Mesbah, M. (2003). Minimum Classification Error Using Time-Frequency Analysis. In: Proceedings of the 3rd IEEE International Symposium on Signal Processing and Information Technology, 2003 (ISSPIT 2003). IEEE International Symposium on Signal Processing and Information Technology (ISSPIT 2003), Maritim Rhein/Main Hotel, Darmstadt, Germany, (717-720). 14-17 December 2003. doi:10.1109/ISSPIT.2003.1341221


Author Breakenridge, C.
Mesbah, M.
Title of paper Minimum Classification Error Using Time-Frequency Analysis
Conference name IEEE International Symposium on Signal Processing and Information Technology (ISSPIT 2003)
Conference location Maritim Rhein/Main Hotel, Darmstadt, Germany
Conference dates 14-17 December 2003
Convener Institute of Electrical and Electronic Engineers (IEEE)
Proceedings title Proceedings of the 3rd IEEE International Symposium on Signal Processing and Information Technology, 2003 (ISSPIT 2003)
Journal name Proceedings of the 3rd Ieee International Symposium On Signal Processing and Information Technology
Place of Publication Piscataway, N.J.
Publisher IEEE
Publication Year 2003
Year available 2003
Sub-type Fully published paper
DOI 10.1109/ISSPIT.2003.1341221
ISBN 0-7803-8292-7
Start page 717
End page 720
Total pages 4
Language eng
Abstract/Summary For certain classes of signals, such as time varying signals, classical classification algorithms are not suitable. Hence, time-frequency hased techniques are employed for classification of these types of signals. In this paper we propose data-driven time frequency representations kernel optimization, that leads to the minimum classification error (MCE) for nonstationary signal classification. Our central issue is to determine the optimal kernel parameters and best distance measure to achieve the MCE performance measure. The minimum classification error achievable using optimized kernels is investigated for two types of nonstationary signals: namely simulated chirp signals and real-life newborn EEG signals. For the EEG signals a classification error as low as 4.6% was achieved.
Subjects 090609 Signal Processing
Q-Index Code E1
Q-Index Status Provisional Code
Institutional Status Unknown

 
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Created: Mon, 30 Mar 2009, 19:47:02 EST by Mary-Anne Marrington on behalf of UQ Centre for Clinical Research