Thesis Type: Postgraduate
Institution Of The Thesis: Middle East Technical University, Graduate School of Natural and Applied Sciences, Graduate School of Natural and Applied Sciences, Turkey
Approval Date: 2022
Thesis Language: English
Student: MEHMET ENES ŞEN
Supervisor: Barış Nakiboğlu
Abstract:
The general framework for active learning is explained. The existing active learning strategies are surveyed. The information-theoretic measures such as the entropy and the mutual information are analyzed as active learning objectives. The use of divergence measures in density-weighted active learning is discussed. A novel density-weighted active learning algorithm, based on Cauchy-Schwarz divergence and entropy, is introduced and compared with the state-of-the-art active learning strategies.