| PROJECT: | Ensemble Methods in Unsupervised and Semi-Supervised Learning | |||||
| ACRONYM: | EMUSuL | |||||
| MAIN OBJECTIVE: | This is a fundamental research project in the area of machine learning. Accordingly, the goals are of methodological, theoretical and algorithmic nature. More specifically, the project aims at:
- developing cluster combination techniques, in which partitions, clusters and even samples can assume different weights in the combination strategies. - integrating common and state-of-the-art clustering algorithms in the combination process - creating a theoretical framework for the analysis of clustering combination techniques and cluster validity - extending combination techniques used in supervised and unsupervised learning to the semi-supervised scenario. - applying the developed methodologies to challenging learning problems (both unsupervised and semi-supervised), such as document classification, web page classification, and gene-expression data analysis. |
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| Reference: | POSC/EEA-SRI/61924/2004 | |||||
| Funding: | FCT/POSC | |||||
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| Team: | Mario Alexandre Teles de Figueiredo, Ana Luisa Nobre Fred, Sandra V. B. Jardim | |||||
| Groups: | Pattern and Image Analysis – Lx | |||||
| Partners: | Instituto Superior de Engenharia do Porto | |||||
| Local Coordinator: | Mario Alexandre Teles de Figueiredo |
This project falls under the following United Nations Strategic Development Goals (SDGs):
No publications associated with this project.