In the task of multiview multilabel (MVML) classification, each object is described by several
heterogeneous view features and annotated with multiple relevant labels. Existing MVML …

In multiview multilabel learning, each object is represented by several heterogeneous
feature representations and is also annotated with a set of discrete nonexclusive labels …

Multi-label learning deals with a kind of problem that the given samples areassociated with
multiple labels simultaneously. Recently, multi-label learning has become a populartopic in …

Multi-label learning aims at assigning a set of appropriate labels to multi-label samples.
Although it has been successfully applied in various domains in recent years, most multi …

Multiview multi-instance multilabel learning (M3L) is a framework for modeling complex
objects. In this framework, each object (or bag) contains one or more instances, is …

Multi-label classification is an important research topic in machine learning, for which
exploiting label dependencies is an effective modeling principle. Recently, probabilistic …

Weak-label learning is an important branch of multi-label learning; it deals with samples
annotated with incomplete (weak) labels. Previous work on weak-label learning mainly …

Multi-view learning combines data from multiple heterogeneous sources and employs their
complementary information to build more accurate models. Multi-instance learning …

Multi-label classification (MLC) is a supervised classification method that allows for a data
instance with more than one class label (or target). Solving MLC is still a challenging task …

Abstract Multi-view Multi-Label Classification (MvMLC), a combination of multi-view learning
and multi-label classification, has garnered significant research interest for its ability to …