Exabytes of data are generated daily by humans, leading to the growing needs for new
efforts in dealing with the grand challenges for multi-label learning brought by big data. For …

Multi-label learning studies the problem where each example is represented by a single
instance while associated with a set of labels simultaneously. During the past decade …

The process of computationally identifying and categorizing opinions expressed in a piece
of text is of great importance to support better understanding and services to online users in …

In multidimensional classification the goal is to assign an instance to a set of different
classes. This task is normally addressed either by defining a compound class variable with …

Extreme multi-label text classification (XMTC) refers to the problem of assigning to each
document its most relevant subset of class labels from an extremely large label collection …

Multi-label classification is an important yet challenging task in natural language processing.
It is more complex than single-label classification in that the labels tend to be correlated …

The condition of the vascular network of human eye is an important diagnostic factor in
ophthalmology. Its segmentation in fundus imaging is a nontrivial task due to variable size of …

Traditional supervised learning makes the closed-world assumption that the classes
appeared in the test data must have appeared in training. This also applies to text learning …

In this paper, we propose a novel deep learning architecture for multi-label zero-shot
learning (ML-ZSL), which is able to predict multiple unseen class labels for each input …

Efficient distributed numerical word representation models (word embeddings) combined
with modern machine learning algorithms have recently yielded considerable improvement …