The Cauchy-Schwarz divergence and entropy-based density-weighted active learning

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METU_Thesis_Mehmet_Enes_Sen_.pdf

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2022-9

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Şen, Mehmet Enes

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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.

Subject Keywords

Active learning, Cauchy-Schwarz divergence, Entropy, Mutual information, Information-theory, Machine learning

URI

https://hdl.handle.net/11511/99757

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Graduate School of Natural and Applied Sciences, Thesis

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Citation Formats

M. E. Şen, “The Cauchy-Schwarz divergence and entropy-based density-weighted active learning,” M.S. - Master of Science, Middle East Technical University, 2022.