Strong ensemble nonequivalence in systems with local constraints

Ensemble Learning with Local Experts

Ensemble learning methods have received considerable attention in the past few years. Various methods for combining several learning experts have been developed and used in different domains of machine learning. Many works have focused on decision fusion of different exports. Some methods try to train all the experts on the same training data and then use statistical techniques to combine the r...

Ensemble of hybrid networks with strong regularization

Ensemble Learning with Local Diversity

The concept of Diversity is now recognized as a key characteristic of successful ensembles of predictors. In this paper we investigate an algorithm to generate diversity locally in regression ensembles of neural networks, which is based on the idea of imposing a neighborhood relation over the set of learners. In this algorithm each predictor iteratively improves its state considering only infor...

Local polymer dynamics under strong connectivity constraints: the dendrimer case.

The characteristics of local motion are explored by molecular dynamics simulations in a series of AB(2)-type dendrimer melts. Systems of generations 3-5 were simulated in a wide temperature range, allowing the assessment of effects associated with molecular size, proximity to the detected glasslike transitions, and the strong connectivity constraints imposed by the dendritic topology. Investiga...

An Incremental Network with Local Experts Ensemble

Ensemble learning algorithms aim to train a group of classifiers to enhance the generalization ability. However, vast of those algorithms are learning in batches and the base classifiers (e.g number, type) must be predetermined. In this paper, we propose an ensemble algorithm called INLEX (Incremental Network with Local EXperts ensemble) to learn suitable number of linear classifiers in an onli...