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Learning with Similarities on Subsets

Ruderman, Avraham Pinchas


Many machine learning models are based on similarities between new examples and previously observed examples. Such models are extremely flexible and can adapt to a wide range of tasks. However, if examples are composed of many variables, then even if we collect a large number of examples, it is possible that no two examples will be significantly similar. This, in turn, means that a learning algorithm may require an unreasonably large number of examples to...[Show more]

CollectionsOpen Access Theses
Date published: 2015
Type: Thesis (PhD)


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