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 …

The aim of multi-output learning is to simultaneously predict multiple outputs given an input.
It is an important learning problem for decision-making since making decisions in the real …

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 …

Social media such as blogs, Facebook, Flickr, etc., presents data in a network format rather
than classical IID distribution. To address the interdependency among data instances …

In multi-label learning, the training set is made up of instances each associated with a set of
labels, and the task is to predict the label sets of unseen instances. In this paper, this …

Social media has reshaped the way in which people interact with each other. The rapid
development of participatory web and social networking sites like YouTube, Twitter, and …

Recommending phrases from web pages for advertisers to bid on against search engine
queries is an important research problem with direct commercial impact. Most approaches …

Multi‐label learning is quite a recent supervised learning paradigm. Owing to its capabilities
to improve performance in problems where a pattern may have more than one associated …

Recently, multi-label learning has received much attention in the applications of image
annotation and classification. However, most existing multi-label learning methods do not …

Multi-label classification algorithms based on supervised learning use all the labeled data to
train classifiers. However, in real life, many of the data are unlabeled, and it is costly to label …