A review of multi-instance learning assumptions


Multi-instance (MI) learning is a variant of inductive machine learning where each learning example contains a bag of instances instead of a single feature vector. The term commonly refers to the supervised setting, where each bag is associated with a label. This type of representation is a natural fit for a number of real-world learning scenarios… (More)
DOI: 10.1017/S026988890999035X



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