Apr 30, 2020 · The existing MLC algorithms usually learn multiple classifiers simultaneously by exploiting the correlations among different labels. However, it ...

Abstract—Multilabel classification (MLC) has received much attention recently. The existing MLC algorithms usually learn multiple classifiers simultaneously ...

May 26, 2020 · An inductive semi-supervised method called Smile is proposed for multi-label classification using incomplete label information. Abstract.

In this paper, the problem of distributed multi-label classification. (MLC) with a small portion of labeled data is considered, and a quantized distributed semi ...

Apr 1, 2017 · We propose a semisupervised MI learning method for multilabel classification. Most MI learning methods treat instances in each bag as ...

In this report we consider the semi-supervised learning problem for multi-label image classification, aiming at ef- fectively taking advantage of both labeled ...

We propose a semisupervised MI learning method for multilabel classification. ... information coming from the most relevant part of the variational distribution.

We propose a novel framework for partial multi-label learning in semi-supervised scenarios by solving the inconsistency between features and labels.

Jun 21, 2024 · In this paper, we propose a novel semi-supervised multi-label dimensionality reduction learning approach based on minimizing redundant correlation of specific ...

Jan 4, 2022 · The common purpose of semi-supervised algorithms is to exploit both labeled data and unlabeled data to create superior classifiers compared to ...