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Classification using linear models with uncertain weights

Manson, G. and Pierce, S.G. and Worden, K. and Chetwynd, D. (2005) Classification using linear models with uncertain weights. In: Eurodyn 2005: 6th International Conference on Structural Dynamics, 2005-09-04 - 2005-09-07, Paris.

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Abstract

Linear models were trained on a simple two-class two-dimensional data set. The network connections, which were crisp, real numbers, were then replaced with interval ranges of real numbers. Crisp input data was propagated through these uncertain-weighted networks to give interval ranges on the output values. The classification rates of the networks could be adjusted by the level of uncertainty in the connections, allowing the user to specify an acceptable misclassification rate and choosing the network with the best corresponding correct classification rate. Vertex propagation, interval arithmetic and affine arithmetic were used to represent the uncertainty in networks with linear and softmax output activation functions and were benchmarked against a simple output threshold approach. It was found that, although the network responses varied, the various techniques returned similar relative classification rates.

Item type: Conference or Workshop Item (Paper)
ID code: 37888
Keywords: classification, linear models, uncertain weights, Electrical engineering. Electronics Nuclear engineering
Subjects: Technology > Electrical engineering. Electronics Nuclear engineering
Department: Faculty of Engineering > Electronic and Electrical Engineering
Related URLs:
    Depositing user: Pure Administrator
    Date Deposited: 24 Feb 2012 16:54
    Last modified: 17 Jul 2013 15:20
    URI: http://strathprints.strath.ac.uk/id/eprint/37888

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