Chair for Computer Aided Medical Procedures & Augmented Reality
Lehrstuhl für Informatikanwendungen in der Medizin & Augmented Reality

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Master thesis: Meta-clustering

Thesis by: Samin Hamidi
Advisor: Azade Farshad
Supervision by: Prof. Dr. Nassir Navab
Due date:


Clustering is a well-known unsupervised learning approach where the data is partitioned into multiple clusters for assignment of pseudo-labels. The number of clusters in data without labels is an unknown parameter that is usually manually set or is calculated by a data scientist using classical approaches. With recent advances in machine learning, these approaches face limitations, thus the need of automatic prediction of number of clusters emerges to move towards a better learning with less supervision. In this thesis we focus on predicting the number of clusters in the k-means clustering method using meta-learning or learning to learn.



Title: Meta-clustering
Student: Samin Hamidi
Director: Prof. Dr. Nassir Navab
Supervisor: Azade Farshad
Type: Master Thesis
Area: Machine Learning, Computer Vision
Status: finished
Thesis (optional):  

Edit | Attach | Refresh | Diffs | More | Revision r1.2 - 21 Oct 2020 - 11:39 - AzadeFarshad