TY  - JOUR
PY  - 1996//
TI  - Predicting criminal recidivism: A comparison of neural network models with statistical methods
JO  - Journal of criminal justice
A1  - Caulkins, Jonathan
A1  - Cohen, John
A1  - Gorr, Wilpen
A1  - Wei, Jiuchang
SP  - 227
EP  - 240
VL  - 24
IS  - 3
N2  - This article applies neural network and conventional statistical models to predicting criminal recidivism. While having promising properties for predicting recidivism, the network models do not exhibit any advantage over the other methods in an application on a well-known data set. Analysis suggests that currently available prediction variables have limited information content for discriminating recidivists, regardless of the models or methods used.<p />
LA  - 
SN  - 0047-2352
UR  - http://dx.doi.org/10.1016/0047-2352(96)00012-8
ID  - ref1
ER  -