Discovering Relevant Sensor Data by Q-Analysis
Iravani, P., 2005. Discovering Relevant Sensor Data by Q-Analysis. Berlin: Springer, pp. 81-92. (Lecture Notes in Computer Science)
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This paper proposes a novel method for supervised classification based on the methodology of Q-analysis. The classification is based on finding `relevant' structures in the features describing the data, and using them to define each of the classes. The features not included in the structural definition of a class are considered as `irrelevant'. The paper uses three diferent data-sets to experimentally validate the method.
|Item Type||Conference or Workshop Items (UNSPECIFIED)|
|Departments||Faculty of Engineering & Design > Mechanical Engineering|
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