This paper seeks to improve low-cost labeling in terms of training set reliability (the fraction of correctly labeled training items) and test set performance for multi-view learning methods. Co-training is a popular multiview learning method that combines highconfidence example selection with low-cost (self) labeling. However, co-training with certain base learning algorithms significantly red...

A concise overview of the fundamentals and the main types of machine ensembles serves to propose a structured perspective for the papers that are included in this special session. The subsequent brief discussion of the works, emphasizing their principal contributions, permits an extraction of a series of suggestions for further research in the fruitful area of ensemble learning.

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This commutation is about training the Xception model for the Kaggle competition “Cdiscount’s Image Classification Challenge”. The paper will briefly describe all methods/code (github.com/ardiloot/CDiscountClassifier) used to train the model for best classification performance. Mainly, the effect of the augmentation (both train and test time) and algebraic ensemble methods were studied. In the ...

Several works showed that biomolecular data integration is a key issue to improve the prediction of gene functions. Quite surprisingly only little attention has been devoted to data integration for gene function prediction through ensemble methods. In this work we show that relatively simple ensemble methods are competitive and in some cases are also able to outperform state-of-the-art data int...

We consider the combination of the outputs of several classifiers trained independently for the same discrimination task. We introduce new results which provide optimal solutions in the case of linear combinations. We compare our solutions to existing ensemble methods and characterize situations where our approach should be preferred.

Article history: Received 27 August 2012 Received in revised form 1 August 2013 Accepted 5 August 2013 Available online 15 August 2013