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 …

Abstract Multi-label Zero-shot Learning (ZSL) is more reasonable and realistic than standard
single-label ZSL because several objects can co-exist in a natural image in real scenarios …

Hybrid strategy, which generalizes a specific single-label algorithm while one or two data
decomposition tricks are applied implicitly or explicitly, has become an effective and efficient …

This article proposes a novel approach for text categorization based on a regularization
extreme learning machine (RELM) in which its weights can be obtained analytically, and a …

In this paper, we propose a filtering method for feature selection called ALOFT (At Least One
FeaTure). The proposed method focuses on specific characteristics of text categorization …

WiSARD belongs to the class of weightless neural networks, and it is based on a neural
model which uses lookup tables to store the function computed by each neuron rather than …

A supervised learning algorithm for quantum neural networks (QNN) based on a novel
quantum neuron node implemented as a very simple quantum circuit is proposed and …

A method for automatic image annotation based on multi-feature fusion and multi-label
learning algorithm was proposed in this paper. In the process of feature fusion, rotation …

In multi-label classification problems, every instance is associated with multiple labels at the
same time. Binary classification, multi-class classification and ordinal regression problems …

Wide use of internet generates huge data which needs proper organization leading to text
categorization. Earlier it was found that a document describes one category. Soon it was …