Multiple Instance Multiple Label Learning

As applications grow more complex, proper data representation becomes more relevant. Experience shows that a representation accurately reflecting existing relations and interactions in the data renders the learning task easier to solve. In this context, multiple instance multiple label learning (MIMLL) appears as a flexible learning framework. The combination of MIL and multi-label learning introduces a greater flexibility and ambiguity in the object representation by providing a natural formulation for representing complicated objects. This chapter provides a general introduction to MIMLL. First, a description and formal definition are presented in Sects. 10.1 and 10.2. The main applications are listed in Sect. 10.3. Appropriate evaluation metrics for MIMLL are described in Sect. 10.4. Section 10.5 presents an overview of the proposed methods and Sect. 10.7 describes some current advances. Finally, Sect. 10.6 describes the Yelp classification challenge.

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