TY - JOUR
T1 - On the determination of probability density functions by using Neural Networks
AU - Garrido, Lluís
AU - Juste, Aurelio
PY - 1998/12/1
Y1 - 1998/12/1
N2 - It is well known that the output of a Neural Network trained to disentangle between two classes has a probabilistic interpretation in terms of the a posteriori Bayesian probability, provided that a unary representation is taken for the output patterns. This fact is used to make Neural Networks approximate probability density functions from examples in an unbinned way, giving a better performance than "standard binned procedures". In addition, the mapped p.d.f. has an analytical expression. © 1998 Elsevier Science B.V.
AB - It is well known that the output of a Neural Network trained to disentangle between two classes has a probabilistic interpretation in terms of the a posteriori Bayesian probability, provided that a unary representation is taken for the output patterns. This fact is used to make Neural Networks approximate probability density functions from examples in an unbinned way, giving a better performance than "standard binned procedures". In addition, the mapped p.d.f. has an analytical expression. © 1998 Elsevier Science B.V.
U2 - 10.1016/S0010-4655(98)00107-6
DO - 10.1016/S0010-4655(98)00107-6
M3 - Article
VL - 115
SP - 25
EP - 31
JO - Computer Physics Communications
JF - Computer Physics Communications
SN - 0010-4655
IS - 1
ER -