Feature reduction using Support Vector Machines for binary gas detection
UNSPECIFIED (2003) Feature reduction using Support Vector Machines for binary gas detection. In: 7th International Work Conference on Artificial and Natural Neural Networks, MENORCA, SPAIN, JUN 03-06, 2003. Published in: ARTIFICIAL NEURAL NETS PROBLEM SOLVING METHODS, PT II, 2687 pp. 798-805.Full text not available from this repository.
Gas sensor (electronic nose) has many different applications, such as fire detection, food quality control or medical application as well as the detection of atmospheric gases. We describe in this paper a signal processing technique using wavelet transform and Support Vector Machines (SVM) for CO and NO2 gas detection and to obtain gas concentration. We propose a low complexity algorithm which can be implemented in a low cost palmtop gas monitor. SVM were used in a twofold way. First, SVM were used to classify the type of gas and then for the estimation of gas concentration.
|Item Type:||Conference Item (UNSPECIFIED)|
|Subjects:||Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software|
|Series Name:||LECTURE NOTES IN COMPUTER SCIENCE|
|Journal or Publication Title:||ARTIFICIAL NEURAL NETS PROBLEM SOLVING METHODS, PT II|
|Editor:||Mira, J and Alvarez, JR|
|Number of Pages:||8|
|Page Range:||pp. 798-805|
|Title of Event:||7th International Work Conference on Artificial and Natural Neural Networks|
|Location of Event:||MENORCA, SPAIN|
|Date(s) of Event:||JUN 03-06, 2003|
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