Several selection methods in the literature are essentially based on an evaluation function that determines whether a model M contributes positively to boost the performances of the whole ensemble. In this paper, we propose ...

Machine learning techniques have been extensively researched in the last years, mainly due to their effectiveness when dealing with recognition or classification applications. Typically, one can comprehend using a Machine ...

Outlier detection and ensemble learning are well established research directions in data mining yet the application of ensemble techniques to outlier detection has been rarely studied. Building an ensemble requires learning ...

In this paper, we propose a technique that uses multimodal interactions of users to generate a more accurate list of recommendations optimized for the user . Our approach is a response to the actual scenario on the Web ...

Convolutional Neural Networks have been widely employed in a diverse range of computer vision-based applications, including image classification, object recognition, and object segmentation. Nevertheless, one weakness of ...

Ensembling machine-learned models has shown to be a useful technique for improving the effectiveness of tasks such as classification, ad-hoc retrieval, and recommendation. Stacking, for instance, learns to weight and combine ...

This paper presents a new ensemble method for learning from non-stationary data streams. In these situations, massive data are constantly generated at high speed and their target function can change over time. The proposed ...