Classification of volatile organic compounds with incremental SVMs and RBF networks
ISCIS'05 Proceedings of the 20th international conference on Computer and Information Sciences
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Gas sensing systems for detection and identification of odorant molecules are of crucial importance in an increasing number of applications. Such applications include environmental monitoring, food quality assessment, airport security, and detection of hazardous gases. We describe a gas sensing system for detecting and identifying volatile organic compounds (VOC), and discuss the unique problems associated with the separability of signal patterns obtained by using such a system. We then present solutions for enhancing the separability of VOC patterns to enable classification. A new incremental learning algorithm that allows new odorants to be learned is also introduced.