Entrepôts, Représentation et Ingénierie des Connaissances
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- Semi-Supervised Structuring of Complex Data hal link

Auteur(s): Rizoiu M.-A.(Corresp.)

Conference: Doctoral Consortium of IJCAI '13 (Beijing, CN, 2013-08-05)
Actes de conférence: Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI '13), vol. p.3239--3240 (2013)

Ref HAL: hal-00866984_v1

The objective of the thesis is to explore how complex data can be treated using unsupervised machine learning techniques, in which additional information is injected to guide the exploratory process. Starting from specific problems, our contributions take into account the different dimensions of the complex data: their nature (image, text), the additional information attached to the data (labels, structure, concept ontologies) and the temporal dimension. A special attention is given to data representation and how additional information can be leveraged to improve this representation.