Stacked generalization is a general method of using a high-level model to combine lower-
level models to achieve greater predictive accuracy. In this paper we address two crucial …

In this paper, we investigate the method of stacked generalization in combining models
derived from different subsets of a training dataset by a single learning algorithm, as well as …

Stacked generalization is a general method of using a high-level model to combine lower-
level models to achieve greater predictive accuracy. In this paper we resolve two crucial …

A problem of bounding the generalization error of a classifier%\break $ f\in\conv (\mathcal
{H}) $, where $\mathcal {H} $ is a" base" class of functions (classifiers), is considered. This …

The approach of combining models learned from multiple batches of data provide an
alternative to the common practice of learning one model from all the available data (ie, the …

We show that adaptive real time dynamic programming extended with the action selection
strategy which chooses the best action according to the latest estimate of the cost function …

In this article, we first explore an intrinsic problem that exists in the models induced by
learning algorithms. Regardless of the selected algorithm, search methodology and …

Combining models learned from multiple batches of data provide an alternative to the
common practice of learning one model from all the available data (ie the data combination …

The approach of combining theories learned from multiple batches of data provide an
alternative to the common practice of learning one theory from all the available data (ie, the …