Yejin Kan

T-MDML: Triplet-based Multiple Distance Metric Learning for Multi-Instance Multi-Label Classification with Label Correlation

The multi-instance multi-label (MIML) problem is a new supervised learning paradigm that has emerged to efficiently represent complex data. Therefore, various similarity-based algorithms have been proposed, but existing algorithms commonly measure similarity by considering only the structural relati…