ML
2000
ACM
13 years 4 months ago
2000
ACM
Capacity control in perceptron decision trees is typically performed by controlling their size. We prove that other quantities can be as relevant to reduce their flexibility and co...
INFORMATICALT
2000
13 years 4 months ago
2000
An estimation of the generalization performance of classifier is one of most important problems in pattern clasification and neural network training theory. In this paper we estima...
IEICET
2007
13 years 4 months ago
2007
In order to obtain better generalization performance in supervised learning, model parameters should be determined appropriately, i.e., they should be determined so that the gener...
IEICET
2007
13 years 4 months ago
2007
Estimating the generalization error is one of the key ingredients of supervised learning since a good generalization error estimator can be used for model selection. An unbiased g...
IEICET
2007
13 years 4 months ago
2007
In order to obtain better learning results in supervised learning, it is important to choose model parameters appropriately. Model selection is usually carried out by preparing a ...
JMLR
2006
13 years 4 months ago
2006
The goal of active learning is to determine the locations of training input points so that the generalization error is minimized. We discuss the problem of active learning in line...
JMLR
2008
13 years 4 months ago
2008
We introduce a computationally feasible, "constructive" active learning method for binary classification. The learning algorithm is initially formulated for separable cl...
JMLR
2008
13 years 4 months ago
2008
Feature ranking is a fundamental machine learning task with various applications, including feature selection and decision tree learning. We describe and analyze a new feature ran...
BMCBI
2008
13 years 4 months ago
2008
Background: There has been recent concern regarding the inability of predictive modeling approaches to generalize to new data. Some of the problems can be attributed to improper m...
ESANN
2003
13 years 6 months ago
2003
The bootstrap resampling method may be efficiently used to estimate the generalization error of a family of nonlinear regression models, as artificial neural networks. The main dif...