Soutenance de thèse Moustapha Cissé 25 Juillet 2014, 10h

Bonjour,

J'ai le plaisir de vous inviter à ma soutenance de thèse intitulée
"Efficient Extreme Classification".

Elle aura lieu au Laboratoire d'Informatique de Paris 6 (LIP6) à
Jussieu, le **vendredi 25 juillet 2014 à 10h00 en salle 25-26/101** et
vous êtes
également chaleureusement conviés au pot qui suivra.

Plan d'accès :

http://www.upmc.fr/fr/universite/campus_et_sites/a_paris_et_en_idf/jussieu.html


Cordialement,
Moustapha Cissé


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Jury
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Eric Gaussier, LIG (Grenoble-France) Reviewer
Georges Paliouras, Demokritos (Athens-Greece), Reviewer
Christophe Marsala, LIP6 (Paris-France), Examinator
Nicolas Usunier UTC/CNRS (Compiegne-France), Examinator
Thierry Artieres LIP6 (Paris-France), Co-advisor
patrick Gallinari LIP6 (Paris-France),Co-advisor




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Résumé de la thèse (l'exposé sera en anglais)
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Humans naturally and instantly recognize relevant objects in images
despite the large number of potential visual concepts. They can also
instantly tell which topics are relevant for a given text document
even though these topics are chosen among thousands of semantic
concepts. This ability to quickly categorize information is an
important aspect of high level intelligence and endowing machines with
it is an important step towards artificial intelligence.

We propose in this thesis new methods to tackle classification
problems with a large number of labels, also called extreme
classification. The proposed approaches aim at reducing the inference
complexity in comparison with the classical methods (such as
one-versus-rest) in order to make learning machines usable in a real
life scenario. We propose two types of methods respectively designed
for single label and multilabel classification.

The first proposed method uses existing hierarchical information among
the categories in order to learn low dimensional binary representation
of the categories. The second type of approaches, dedicated to
multilabel problems, adapts the framework of Bloom Filters to
represent subsets of labels with sparse low dimensional binary
vectors. For both methods, binary classifiers are learned to predict
the new low dimensional representation of the categories and several
algorithms are also proposed to recover the set of relevant labels.
Large scale experiments validate the methods.