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 …

Multilabel learning has become a relevant learning paradigm in the past years due to the
increasing number of fields where it can be applied and also to the emerging number of …

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 …

Nowadays, with emerge the multi-label datasets, the multi-label learning processes attracted
interest and increasingly applied to different fields. In such learning processes, unlike single …

This paper presents the input convex neural network architecture. These are scalar-valued
(potentially deep) neural networks with constraints on the network parameters such that the …

The objective in extreme multi-label learning is to train a classifier that can automatically tag
a novel data point with the most relevant subset of labels from an extremely large label set …

This book is concerned with the classification of multilabeled data and other tasks related to
that subject. The goal of this chapter is to formally introduce the problem, as well as to give a …

Multi-label classification (MLC) has recently attracted increasing interest in the machine
learning community. Several studies provide surveys of methods and datasets for MLC, and …

The objective in extreme multi-label classification is to learn a classifier that can
automatically tag a data point with the most relevant subset of labels from a large label set …

Multi-label learning has received significant attention in the research community over the
past few years: this has resulted in the development of a variety of multi-label learning …