Randa Kassab

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Most conventional learning algorithms require both positive and negative training data for achieving accurate classification results. However, the problem of learning classifiers from only positive data arises in many applications where negative data are too costly, difficult to obtain, or not available at all. This paper describes a new machine learning(More)
This paper presents an original approach to modelling user’s information need in text filtering environment. This approach relies on a specific novelty detection model which allows both accurate learning of user’s profile and evaluation of the coherency of user’s behaviour during his interaction with the system. Thanks to an online learning algorithm, the(More)
Many episodic memory studies have critically implicated the hippocampus in the rapid binding of sensory information from the perception of the external environment, reported by exteroception. Other structures in the medial temporal lobe, especially the amygdala, have been more specifically linked with emotional dimension of episodic memories, reported by(More)
The consideration of underlying analysis of user's information need is a key requirement in an intelligent filtering environment. However, the majority of current approaches to filtering are relevance-oriented, rather than user-oriented. This is partly because they are issued from fields that have somewhat different perspectives from that of information(More)
Neuronal models of associative memories are recurrent networks able to learn quickly patterns as stable states of the network. Their main acknowledged weakness is related to catastrophic interference when too many or too close examples are stored. Based on biological data we have recently proposed a model resistant to some kinds of interferences related to(More)