An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Support Vector Machines: Training and Applications
Support Vector Machines: Training and Applications
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
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Electronic noses (E-nose) have gained popularity in various applications such as food inspection, cosmetics quality control [1], toxic vapor detection to counter terrorism, detection of Improvised Explosive Devices (IED), narcotics detection, etc. In the paper, we summarized our results on the application of Support Vector Machines (SVM) to gas detection and classification using E-nose. First, based on experimental data from Jet Propulsion Lab. (JPL), we created three different data sets based on different pre-processing techniques. Second, we used SVM to detect gas sample data from non-gas background data, and used three sensor selection methods to improve the detection rate. We were able to achieve 85% correct detection of gases. Third, SVM gas classifier was developed to classify 15 different single gases and mixtures. Different sensor selection methods were applied and FSS & BSS feature selection method yielded the best performance.