When distributed data sources have different contexts the problem of Distributed Regression becomes severe. It is the underlying law of probability that constitutes the context of a source. A new Distributed Regression System is presented, which makes use of a discrete representation of the probability density functions (pdfs). Neighborhoods of similar datasets are detected by comparing their a...

We propose a novel ensemble learning algorithm called Triskel, which has two interesting features. First, Triskel learns an ensemble of classifiers that are biased to have high precision (as opposed to, for example, boosting, where the ensemble members are biased to ignore portions of the instance space). Second, Triskel uses weighted voting like most ensemble methods, but the weights are assig...

Ensemble learning is an intensively studies technique in machine learning and pattern recognition. Recent work in computational biology has seen an increasing use of ensemble learning methods due to their unique advantages in dealing with small sample size, high-dimensionality, and complexity data structures. The aim of this article is two-fold. First, it is to provide a review of the most wide...

Supervised online learning with an ensemble of students randomized by the choice of initial conditions is analyzed. For the case of the perceptron learning rule, asymptotically the same improvement in the generalization error of the ensemble compared to the performance of a single student is found as in Gibbs learning. For more optimized learning rules, however, using an ensemble yields no impr...

A number of ensemble algorithms for solving multi-label classification problems have been proposed in recent years. Diversity among the base learners is known to be important for constructing a good ensemble. In this paper we define a method for introducing diversity among the base learners of one of the previously presented multi-label ensemble classifiers. An empirical comparison on 10 datase...

Boosting is a kind of ensemble methods which produce a strong learner that is capable of making very accurate predictions by combining rough and moderately inaccurate learners (which are called as base learners or weak learners). In particular, Boosting sequentially trains a series of base learners by using a base learning algorithm, where the training examples wrongly predicted by a base learn...

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...

The present paper introduces a newMultiling text summary evaluation method. This method relies on machine learning approach which operates by combining multiple features to build models that predict the human score (overall responsiveness) of a new summary. We have tried several single and “ensemble learning” classiers to build the best model. We have experimented our method in summary level ev...

For the opinion analysis task on traditional Chinese texts at NTCIR-7, supervised approaches and ensemble techniques have been used and compared in our participating system. Two kinds of supervised approaches were employed here: 1) the supervised lexicon-based approach, and 2) machine learning approaches. Ensemble techniques were also used to combine the results given by different approaches. B...

Relational networks often evolve over time by the addition, deletion, and changing of links, nodes, and attributes. However, accurately incorporating the full range of temporal dependencies into relational learning algorithms remains a challenge. We propose a novel framework for discovering temporal-relational representations for classification. The framework considers transformations over all ...