Ensemble methods improve accuracy by combining the predictions of a set of different hypotheses. However, there are two important shortcomings associated with ensemble methods. Huge amounts of memory are required to store a set of multiple hypotheses and, more importantly, comprehensibility of a single hypothesis is lost. In this work, we devise a new method to extract one single solution from ...

We describe the application of ensemble methods to binary classification problems on two pharmaceutical compound data sets. Several variants of single and ensembles models of k-nearest neighbors classifiers, support vector machines (SVMs), and single ridge regression models are compared. All methods exhibit robust classification even when more features are given than observations. On two data s...

In this paper we i n v estigate a number of ensemble methods for improving the performance of connectionist acoustic models for large vocabulary continuous speech recognition. We discuss boosting, a data selection technique which results in an ensemble of models, and mixtures-of-experts. These techniques have been applied to multi-layer perceptron acoustic models used to build a hybrid connecti...

Topology preserving mappings are great tools for data visualization and inspection in large datasets. This research presents a study of the combination of different ensemble training techniques with a novel summarization algorithm for ensembles of topology preserving models. The aim of these techniques is the increase of the truthfulness of the visualization of the dataset obtained by this kind...

Statistical estimates can often be improved by fusion of data from several different sources. One example is so-called ensemble methods which have been successfully applied in areas such as machine learning for classification and clustering. In this paper, we present an ensemble method to improve community detection by aggregating the information found in an ensemble of community structures. Th...

Accent classification technologies directly influence the performance of the state-of-the-art speech recognition system. In this paper, we propose a novel scheme for accent classification, which uses decision-templates (DT) ensemble algorithm to combine base classifiers built on acoustic feature subsets. Different feature subsets can provide sufficient diversity among base classifiers, which is...

An ensemble consists of a set of individually trained classifiers (such as neural networks or decision trees) whose predictions are combined when classifying novel instances. Previous research has shown that an ensemble is often more accurate than any of the single classifiers in the ensemble. Bagging (Breiman, 1996c) and Boosting (Freund & Schapire, 1996; Schapire, 1990) are two relatively new...

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 ...

Recent ensemble clustering techniques have been shown to be effective in improving the accuracy and stability of standard clustering algorithms. However, an inherent drawback of these techniques is the computational cost of generating and combining multiple clusterings of the data. In this paper, we present an efficient kernel-based ensemble clustering method suitable for application to large, ...