Research Lines
The lab's research focuses on machine learning and its applications. We are interested in the foundations of machine learning and how it is used to solve challenging problems.
Hybrid Kernel Methods
Topics of interest:
- Topic 1:
Effective and efficient kernel methods that compete on par with deep learning. - Topic 2:
Methods for learning a mapping between the features of the input sample and the labels, which is later used to predict labels for unannotated instances.
Related Publications (Thesis):
| Type | Year | Author | Title |
|---|---|---|---|
| Master Thesis | 2017 | Joseph Alejandro Gallego Mejía | Robust unsupervised learning using Kernels |
| Master Thesis | 2016 | Lady Viviana Beltrán Beltrán | Online Supervised Non-linear Dimensionality Reduction |
| Master Thesis | 2015 | Andrés Esteban Páez Torres | Online Kernel Matrix Factorization |
| Master Thesis | 2010 | Juan Carlos Galeano Huertas | Kernel-based Model for Artificial Immune Networkseng |