Multiple-instance learning from unlabeled bags with pairwise similarity

• المؤلفون الرئيسيون: Feng, Lei, Shu, Senlin, Cao, Yuzhou, Tao, Lue, Wei, Hongxin, Xiang, Tao, An, Bo, Niu, Gang
• مؤلفون آخرون: School of Computer Science and Engineering
• التنسيق: مقال
• اللغة: English
• منشور في: 2023
• الموضوعات: Engineering::Computer science and engineering; Multiple-Instance Learning; Empirical Risk Minimization
• الوصول للمادة أونلاين: https://hdl.handle.net/10356/172864

In multiple-instance learning (MIL), each training example is represented by a bag of instances. A training bag is either negative if it contains no positive instances or positive if it has at least one positive instance. Previous MIL methods generally assume that training bags are fully labeled. Ho...