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This paper presents an architecture for solving generically the problem of extracting the relevant features of a given task in a programming by demonstration framework and the problem of generalizing the acquired knowledge to various contexts. We validate ...
Directory services are a genuine constituent of any distributed architecture which facilitate binding attributes to names and then querying this information, that is, announcing and discovering resources. In such contexts, especially in a business environm ...
In this paper, we study the problem of content-based social network discovery among people who frequently appear in world news. Google news is used as the source of data. We describe a probabilistic framework for associating people with groups. A low-dimen ...
With the rapid expansion in the use of computers for producing digitalized textual documents, the need of automatic systems for organizing and retrieving the information contained in large databases has become essential. In general, information retrieval s ...
With the rapid expansion in the use of computers for producing digitalized textual documents, the need of automatic systems for organizing and retrieving the information contained in large databases has become essential. In general, information retrieval s ...
Text categorization is intrinsically a supervised learning task, which aims at relating a given text document to one or more predefined categories. Unfortunately, labeling such databases of documents is a painful task. We present in this paper a method tha ...
Email plays an important role as a medium for the spread of information, ideas, and influence among its users. We present a framework to learn topic-based interactions between pairs of email users, i.e., the extent to which the email topic dynamics of one ...
The growing number of web services advocates distributed discovery infrastructures which are semantics-enabled and support quality of service (QoS). In this paper, we introduce a novel approach for semantic discovery of web services in P2P-based registries ...
We propose the use of latent space models applied to local invariant features for object classification. We investigate whether using latent space models enables to learn patterns of visual co-occurrence and if the learned visual models improve performance ...
We address the problem of segmentation and recognition of sequences of multimodal human interactions in meetings. These interactions can be seen as a rough structure of a meeting, and can be used either as input for a meeting browser or as a first step tow ...