60 search results - page 1 / 12 » Bias in error estimation when using cross-validation for mod... |
BMCBI
2006
13 years 4 months ago
2006
Background: Cross-validation (CV) is an effective method for estimating the prediction error of a classifier. Some recent articles have proposed methods for optimizing classifiers...
IJCNN
2006
IEEE
13 years 11 months ago
2006
IEEE
Abstract— While the model parameters of many kernel learning methods are given by the solution of a convex optimisation problem, the selection of good values for the kernel and r...
IJCNN
2006
IEEE
13 years 11 months ago
2006
IEEE
We propose a new semi-supervised model selection method that is derived by applying the structural risk minimization principle to a recent semi-supervised generalization error bou...
ICML
1994
IEEE
13 years 8 months ago
1994
IEEE
Model selection is important in many areas of supervised learning. Given a dataset and a set of models for predicting with that dataset, we must choose the model which is expected...
COLT
1995
Springer
13 years 8 months ago
1995
Springer
The Noise Sensitivity Signature (NSS), originally introduced by Grossman and Lapedes (1993), was proposed as an alternative to cross validation for selecting network complexity. I...