000			01522naa a2200169uu 4500
001			5061511361310
003			OSt
005			20190211160004.0
008			050615s2005 xx ||||gr |0|| 0 eng d
100	1		_aDE MARCHI, Scott; GELPI, Christopher; GRYNAVISKI, Jeffrey D _921370
245	1	0	_aUntangling neural nets
260			_aNew York : _bCambridge University Press, _cMay 2004
520	3		_aBeck, King, and Zeng (2000) offer both a sweeping critique of the quantitative security studies field and a bold new direction for future research. Despite important strengths in their work, we take issue with three aspects of their research: (1) the substance of the logit model they compare to their neural network, (2) the standards they use for for assessing forecasts, and (3) the theoretical and model-building implications of the nonparametric approach represented by neural networks. We replicate and extend their analysis by estimating a more comoplete logit model and comparing it both to a neural network and to a linear discriminant analysis. our work reveals that neural networks do not perform substantially better than either the logit or the linear discriminant estimators. Given this result, we argue that more traditional approaches should be relied upon due to their enhanced ability to test hypotheses.
773	0	8	_tAmerican Political Science Review _g98, 2, p. 371-378 _dNew York : Cambridge University Press, May 2004 _xISSN 0003-0554 _w
942			_cS
998			_a20050615 _b1136^b _cTiago
999			_aConvertido do Formato PHL _bPHL2MARC21 1.1 _c13259 _d13259
041			_aeng