Similarity-based Multi-label Learning
نویسندگان
چکیده
Multi-label classification is an important learning problemwith many applications. In this work, we propose a principled similarity-based approach for multi-label learning called SML. We also introduce a similarity-based approach for predicting the label set size. The experimental results demonstrate the effectiveness of SML for multi-label classification where it is shown to compare favorably with a wide variety of existing algorithms across a range of evaluation criterion.
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عنوان ژورنال:
- CoRR
دوره abs/1710.10335 شماره
صفحات -
تاریخ انتشار 2017