Title of article :
Model-based MCE bound to the true Bayes error
Author/Authors :
R.، Schulter, نويسنده , , H.، Ney, نويسنده ,
Issue Information :
روزنامه با شماره پیاپی سال 2001
Abstract :
We show that the minimum classification error (MCE) criterion gives an upper bound to the true Bayesʹ error rate independent of the corresponding model distribution. In addition, we show that model-free optimization of the MCE criterion leads to a closed form solution in the asymptotic case of infinite training data. While leading to the Bayesʹ error rate, the resulting model distribution differs from the true distribution. This suggests that the structure of model distributions trained with the MCE criterion should differ from the structure of the true distributions, as they are usually used in statistical pattern recognition
Journal title :
IEEE Signal Processing Letters
Journal title :
IEEE Signal Processing Letters