In this paper we extend a form of kernel ridge regression for data characterised by a heteroscedastic noise process (introduced in Foxall et al. ) in order to provide approximately unbiased estimates of the conditional variance of the target distribution. This is achieved by the use of the leave-one-out cross-validation estimate of the conditional mean when fitting the model of the conditional variance. The elimination of this bias is demonstrated on synthetic dataset where the true conditional variance is known.
|Number of pages||6|
|Publication status||Published - Apr 2003|
|Event||Proceedings of the European Symposium on Artificial Neural Networks (ESANN-2003) - Bruges, Belgium|
Duration: 23 Apr 2003 → 25 Apr 2003
|Conference||Proceedings of the European Symposium on Artificial Neural Networks (ESANN-2003)|
|Period||23/04/03 → 25/04/03|