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Information Fusion and Ensembles in Machine Learning
(2019)
[Abstract] Traditionally, machine learning methods have used a single learning model to solve
a particular problem. However, the idea of combining multiple models instead of a
single one to solve a problem has its rationale ...
New scalable machine learning methods: beyond classification and regression
(2019)
[Abstract]
The recent surge in data available has spawned a new and promising age of machine
learning. Success cases of machine learning are arriving at an increasing rate as some
algorithms are able to leverage immense ...
Algorithms for Sleep Medicine
(2019)
[Abstract] Sleep disorders a ect a signi cant part of our population causing problems that go
from daytime sleepiness to severe, life-threatening conditions. Fortunately, physicians
can diagnose them and propose a treatment ...
Diabetic Macular Edema Characterization by Automatic Analysis of Optical Coherence Tomography
(2019)
[Abstract] Diabetic Macular Edema (DME) is one of the most important complications of
diabetes and a leading cause of preventable blindness in the developed countries.
Among the di erent image modalities, Optical Coherence ...
Seeking robustness in a multilingual world: from pipelines to embeddings
(2019)
[Abstract] In this dissertation, we study two approaches to overcome the challenges posed by processing
user-generated non-standard multilingual text content as it is found on the Web nowadays.
Firstly, we present a ...
Information retrieval models for recommender systems
(2019)
[Abstract]
Information retrieval addresses the information needs of users by delivering
relevant pieces of information but requires users to convey their
information needs explicitly. In contrast, recommender systems ...