Experiences in Pattern Recognition for Machine Olfaction

Simple item page
dc.contributor.authorBessant, Conrad M.
dc.date.accessioned2013-08-02T11:47:49Z
dc.date.available2013-08-02T11:47:49Z
dc.date.issued2011
dc.description.abstractPattern recognition is essential for translating complex olfactory sensor responses into simple outputs that are relevant to users. Many approaches to pattern recognition have been applied in this field, including multivariate statistics (e.g. discriminant analysis), artificial neural networks (ANNs) and support vector machines (SVMs). Reviewing our experience of using these techniques with many different sensor systems reveals some useful insights. Most importantly, it is clear beyond any doubt that the quantity and selection of samples used to train and test a pattern recognition system are by far the most important factors in ensuring it performs as accurately and reliably as possible. Here we present evidence for this assertion and make suggestions for best practice based on these findings.en_UK
dc.identifier.citationProceedings of the 14th International Symposium on Olfaction and Electronic Nose, New York City, NY, USA, 2-5 May 2011, Pages 9-10. Editor: Perena Gouma.en_UK
dc.identifier.isbn9780735409200
dc.identifier.urihttp://dspace.lib.cranfield.ac.uk/handle/1826/8021
dc.language.isoenen_UK
dc.publisherAmerican Institute of Physicsen_UK
dc.relation.ispartofseriesAIP Conference Proceedingsen_UK
dc.subjectChemometricsen_UK
dc.subjectvalidationen_UK
dc.subjectdesign of experiments.en_UK
dc.titleExperiences in Pattern Recognition for Machine Olfactionen_UK
dc.typeConference paperen_UK

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Pattern_recognition_for_machine_Olfacation-2011.pdf
Size:
78.25 KB
Format:
Adobe Portable Document Format
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.79 KB
Format:
Item-specific license agreed upon to submission
Description:
Download

Collections

Staff publications - Cranfield Health