Partial label learning (PLL) is an emerging framework in weakly supervised machine
learning with broad application prospects. It handles the case in which each training …

Partial label learning (PLL), which refers to the classification task where each training
instance is ambiguously annotated with a set of candidate labels, has been recently studied …

Partial-label learning (PLL) is a multi-class classification problem, where each training
example is associated with a set of candidate labels. Even though many practical PLL …

Partial label learning (PLL) is a typical weakly supervised learning problem, where each
training example is associated with a set of candidate labels among which only one is true …

Partial-label learning (PLL) is a typical weakly supervised learning problem, where each
training instance is equipped with a set of candidate labels among which only one is the true …

As an important branch of weakly supervised learning, partial label learning deals with data
where each instance is assigned with a set of candidate labels, whereas only one of them is …

Partial multi-label learning (PML) deals with problems where each instance is assigned with
a candidate label set, which contains multiple relevant labels and some noisy labels. Recent …

Partial-label learning (PLL) is a peculiar weakly-supervised learning task where the training
samples are generally associated with a set of candidate labels instead of single ground …

Partial label learning refers to the issue that each training sample corresponds to a
candidate label set containing only one valid label. Feature selection can be viewed as an …

Partial label learning aims to induce a multi-class classifier from training examples where
each of them is associated with a set of candidate labels, among which only one is the …