Publication: Research - peer-review › Article in proceedings – Annual report year: 2012
Development of sensors and systems for detection of chemical compounds is an important challenge with applications in areas such as anti-terrorism, demining, and environmental monitoring. A newly developed colorimetric sensor array is able to detect explosives and volatile organic compounds; however, each sensor reading consists of hundreds of pixel values, and methods for combining these readings from multiple sensors must be developed to make a classification system. In this work we examine two distance based classification methods, K-Nearest Neighbor (KNN) and Gaussian process (GP) classification, which both rely on a suitable distance metric. We evaluate a range of different distance measures and propose a method for sensor fusion in the GP classifier. Our results indicate that the best choice of distance measure depends on the sensor and the chemical of interest.
|Title||2012 IEEE International Workshop on Machine Learning for Signal Processing (MLSP)|
|Number of pages||6|
|Place of publication||978-1-4673-1025-3|
|Conference||2012 IEEE International Workshop on Machine Learning for Signal Processing (MLSP)|
|Period||23/10/12 → 26/10/12|
|Name||Machine Learning for Signal Processing|
|Citations||Web of Science® Times Cited: No match on DOI|
- Hausdorff distance, Hellinger distance, Chemo–selective compounds, Feature extraction, K–nearest neighbor classification, Gaussian Process Classification
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