Semi-supervised structuring of complex data
Date
2013
Authors
Rizoiu, Marian-Andrei
Journal Title
Journal ISSN
Volume Title
Publisher
AAAI Press
Abstract
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.
Description
Keywords
Keywords: Complex data; Data representations; Semi-supervised; Specific problems; Temporal dimensions; Unsupervised machine learning; Artificial intelligence; Learning systems
Citation
Collections
Source
IJCAI International Joint Conference on Artificial Intelligence
Type
Conference paper
Book Title
Entity type
Access Statement
Open Access