Semi-supervised structuring of complex data
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...[Show more]
|Collections||ANU Research Publications|
|Source:||IJCAI International Joint Conference on Artificial Intelligence|
|Access Rights:||Open Access|
|01_Rizoiu_Semi-supervised_structuring_of_2013.pdf||493.42 kB||Adobe PDF|
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