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Novel applications in unstructured and non-stationary human environments require robots that learn from experience and adapt autonomously to changing conditions. Predictive models therefore not only need to be accurate, but should also be updated increment ...
Learning a visual object category from few samples is a compelling and challenging problem. In several real-world applications collecting many annotated data is costly and not always possible. However a small training set does not allow to cover the high i ...
Adaptive networks consist of a collection of agents with adaptation and learning abilities. The agents interact with each other on a local level and diffuse information across the network through their collaboration. In this work, we consider two types of ...
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Many classes of objects can now be successfully detected with statistical machine learning techniques. Faces, cars and pedestrians, have all been detected with low error rates by learning their appearance in a highly generic manner from extensive training ...
Machine Learning techniques play an increasingly vital role in the analysis of Biomedical imagery, as in all other areas of Computer Vision. However, in this specific context, they suffer from the fact that experimental conditions and protocols change ofte ...
This paper introduces a method to predict and analyse students' mathematical performance by detecting distinguishable subgroups of children who share similar learning patterns. We employ pairwise clustering to analyse a comprehensive dataset of user intera ...
Networks are everywhere and we are confronted with many networks in our daily life. Networks such as Internet, World Wide Web, social, biological and economical networks have been subject to extensive studies in the last decade. The volume of publications ...
We propose a semiautomatic procedure to generate land cover maps from remote sensing images. The proposed algorithm starts by building a hierarchical clustering tree, and exploits the most coherent pixels with respect to the available class information. Fo ...
The vast majority of transfer learning methods proposed in the visual recognition domain over the last years ad- dresses the problem of object category detection, assuming a strong control over the priors from which transfer is done. This is a strict condi ...
The vast majority of transfer learning methods proposed in the visual recognition domain over the last years ad- dresses the problem of object category detection, assuming a strong control over the priors from which transfer is done. This is a strict condi ...
