Neural network objective functions for detection problems

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dc.contributor.authorWeber David
dc.contributor.authorBreitenbach Jaco
dc.date.accessioned2011-05-15T15:57:31Z
dc.date.available2011-05-15T15:57:31Z
dc.date.issued1997
dc.description.abstractWe examine the effects of the choice of neural network objective (criterion) functions on the ability of the neural network to perform detection. The experiments are performed using a multilayer perceptron with the mean square error, classification figure of merit (CFM), maximally flat CFM and the modified perceptron error objective functions. We develop a thresholding scheme for the outputs of the neural network in order to obtain receiver operating characteristic (ROC) curves for the various objective functions. We perform preliminary tests on a breast cancer cell detection problem.
dc.description.versionConference Paper
dc.identifier.citationProceedings of the South African Symposium on Communications and Signal Processing, COMSIG
dc.identifier.urihttp://hdl.handle.net/10019.1/10445
dc.subjectAlgorithms
dc.subjectError detection
dc.subjectProbability
dc.subjectClassification figure of merit (CFM)
dc.subjectReceiver operating characteristics (ROC) curves
dc.subjectNeural networks
dc.titleNeural network objective functions for detection problems
dc.typeConference Paper
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