Backpropagation and Regression: Comparative Utility for Neuropsychologists (bibtex)
@article{parsons_backpropagation_2004,
	title = {Backpropagation and {Regression}: {Comparative} {Utility} for {Neuropsychologists}},
	volume = {26},
	url = {http://ict.usc.edu/pubs/Backpropagation%20and%20Regression-%20Comparative%20Utility%20for%20Neuropsychologists.pdf},
	abstract = {The aim of this research was to compare the data analytic applicability of a backpropagated neural network with that of regression analysis. Thirty individuals between the ages of 64 and 86 (Mean age = 73.6; Mean years education = 15.4; \% women = 50) participated in a study designed to validate a new test of spatial ability administered in virtual reality. As part of this project a standard neuropsychological battery was administered. Results from the multiple regression model (R2 = .21, p {\textbackslash}textbackslashtextless .28; Standard Error = 18.01) were compared with those of a backpropagated ANN (R2 = .39, p {\textbackslash}textbackslashtextless .02; Standard Error = 13.07). This 18\% increase in prediction of a common neuropsychological problem demonstrated that an ANN has the potential to outperform a regression.},
	number = {1},
	journal = {Journal of Clinical and Experimental Neuropsychology},
	author = {Parsons, Thomas D. and Rizzo, Albert and Buckwalter, John Galen},
	year = {2004},
	keywords = {MedVR},
	pages = {95--104}
}
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