A training algorithm for optimal margin classifiers
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
The nature of statistical learning theory
The nature of statistical learning theory
The how and why of electronic noses
IEEE Spectrum
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Electronic nose based tea quality standardization
Neural Networks - 2003 Special issue: Advances in neural networks research — IJCNN'03
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Which is the best multiclass SVM method? an empirical study
MCS'05 Proceedings of the 6th international conference on Multiple Classifier Systems
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Detection of hazardous gases has several commercial and industrial applications. Surface Acoustic Wave(SAW) sensors can provide chemical signatures of gases. A set of SAW sensors in conjunction with a suitably trained pattern recognition engine can work as an Electronic Nose(E-Nose) for a set of gases. For the best performance, the sensors used in the nose must have optimal responses to all the target gases. We first present a method for target gas detection from the sensed data and subsequently describe two novel candidate algorithms used for selection of a subset of sensors from a given set of sensors for an electronic nose.