Multiple-Instance Learning by Boosting Infinitely Many Shapelet-based Classifiers

20 Nov 2018Daiki SuehiroKohei HatanoEiji TakimotoShuji YamamotoKenichi BannaiAkiko Takeda

We propose a new formulation of Multiple-Instance Learning (MIL). In typical MIL settings, a unit of data is given as a set of instances called a bag and the goal is to find a good classifier of bags based on similarity from a single or finitely many "shapelets" (or patterns), where the similarity of the bag from a shapelet is the maximum similarity of instances in the bag... (read more)

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