Gutachter*in / Betreuer*in
Hammer, BarbaraUniBi; Sperduti, Alessandro; Schmidt-Thieme, Lars
Abstract / Bemerkung
Distance measures form a backbone of machine learning and information retrieval in many application fields such as computer vision, natural language processing, and biology. However, general-purpose distances may fail to capture semantic particularities of a domain, leading to wrong inferences downstream. Motivated by such failures, the field of metric learning has emerged. Metric learning is concerned with learning a distance measure from data which pulls semantically similar data closer together and pushes semantically dissimilar data further apart. Over the past decades, metric learning approaches have yielded state-of-the-art results in many applications. Unfortunately, these successes are mostly limited to vectorial data, while metric learning for structured data remains a challenge. In this thesis, I present a metric learning scheme for a broad class of sequence edit distances which is compatible with any differentiable cost function, and a scalable, interpretable, and effective tree edit distance learning scheme, thus pushing the boundaries of metric learning for structured data. Furthermore, I make learned distances more useful by providing a novel algorithm to perform time series prediction solely based on distances, a novel algorithm to infer a structured datum from edit distances, and a novel algorithm to transfer a learned distance to a new domain using only little data and computation time. Finally, I apply these novel algorithms to two challenging application domains. First, I support students in intelligent tutoring systems. If a student gets stuck before completing a learning task, I predict how capable students would proceed in their situation and guide the student in that direction via edit hints. Second, I use transfer learning to counteract disturbances for bionic hand prostheses to make these prostheses more robust in patients' everyday lives.
Urheberrecht / Lizenzen
Paaßen B. Metric Learning for Structured Data. Bielefeld: Universität Bielefeld; 2019.
Paaßen, B. (2019). Metric Learning for Structured Data. Bielefeld: Universität Bielefeld. doi:10.4119/unibi/2935545
Paaßen, Benjamin. 2019. Metric Learning for Structured Data. Bielefeld: Universität Bielefeld.
Paaßen, B. (2019). Metric Learning for Structured Data. Bielefeld: Universität Bielefeld.
Paaßen, B., 2019. Metric Learning for Structured Data, Bielefeld: Universität Bielefeld.
B. Paaßen, Metric Learning for Structured Data, Bielefeld: Universität Bielefeld, 2019.
Paaßen, B.: Metric Learning for Structured Data. Universität Bielefeld, Bielefeld (2019).
Paaßen, Benjamin. Metric Learning for Structured Data. Bielefeld: Universität Bielefeld, 2019.
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diss.pdf 2.24 MB