Class-instance label propagation algorithms have been successfully used to fuse information from multiple sources in order to enrich a set of unlabeled instances with class labels...
Zornitsa Kozareva, Konstantin Voevodski, Shang-Hua...
Multiple-instance problems arise from the situations where training class labels are attached to sets of samples (named bags), instead of individual samples within each bag (called...
Supervised and unsupervised learning methods have traditionally focused on data consisting of independent instances of a single type. However, many real-world domains are best des...
Type classes have found a wide variety of uses in Haskell programs, from simple overloading of operators (such as equality or ordering) to complex invariants used to implement typ...
In multiple-instance learning (MIL), an individual example is called an instance and a bag contains a single or multiple instances. The class labels available in the training set ...