{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/19623"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/19623","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"A probabilistic reasoning-based approach to machine learning","abstract":"This thesis describes a novel approach to machine learning, based on the principle of learning by reasoning. Current learning systems have significant limitations like brittleness, i.e. the deterioration of performance on a different domain or problem and lack of power required for handling real-world learning problems. The goal of my research was to develop an approach in which many of these limitations are overcome in a unified, coherent and general frame-work. 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