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 …

Multi-label classification is a practical yet challenging task in machine learning related fields,
since it requires the prediction of more than one label category for each input instance. We …

In many practical applications of supervised learning the task involves the prediction of
multiple target variables from a common set of input variables. When the prediction targets …

User identity linkage across social platforms is an important problem of great research
challenge and practical value. In real applications, the task often assumes an extra degree …

Multi-label classification deals with the problem where each instance can be associated with
a set of class labels. However, in many real-world applications, the number of class labels …

Multi-output learning with the task of simultaneously predicting multiple outputs for an input
has increasingly attracted interest from researchers due to its wide application. The nearest …

Multi-label image classification aims to predict multiple labels for a single image which
contains diverse content. By utilizing label correlations, various techniques have been …

The recent deep cross-modal hashing (DCMH) has achieved superior performance in
effective and efficient cross-modal retrieval and thus has drawn increasing attention …

Many applications, such as human action recognition and object detection, can be
formulated as a multiclass classification problem. One-vs-rest (OVR) is one of the most …

In this paper, we present a novel probabilistic label enhancement model to tackle multi-label
image classification problem. Recognizing multiple objects in images is a challenging …