Results 241 to 250 of about 21,136,484 (286)

LCBM: A Multi-View Probabilistic Model for Multi-Label Classification

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021
Multi-label classification is an important research topic in machine learning, for which exploiting label dependencies is an effective modeling principle. Recently, probabilistic models have shown great potential in discovering dependencies among labels. In this paper, motivated by the recent success of multi-view learning to improve the generalization
Shiliang Sun
exaly   +4 more sources

Non-Aligned Multi-View Multi-Label Classification via Learning View-Specific Labels

IEEE Transactions on Multimedia, 2023
Dawei Zhao, Dong Sun, Qingwei Gao
exaly   +2 more sources

Federated Multi-View Multi-Label Classification

IEEE Transactions on Big Data
Yongjian Deng, Yipeng Wang
exaly   +2 more sources

Reliable Representation Learning for Incomplete Multi-View Missing Multi-Label Classification

IEEE Transactions on Pattern Analysis and Machine Intelligence
Accepted by TPAMI.
Jie Wen, Min Zhang, Yong Xu
exaly   +5 more sources

An ensemble-based approach for multi-view multi-label classification

Progress in Artificial Intelligence, 2016
Multi-label classification with multiple data views is a recent research field not much explored. This more flexible learning approach allows each pattern to be represented by several sets of attributes and each pattern can have simultaneously associated several labels.
Eva Gibaja, Jose M. Moyano, Sebastián Ventura   +2 more
openaire   +1 more source

Multi-view multi-label active learning for image classification

2009 IEEE International Conference on Multimedia and Expo, 2009
Image classification is an important topic in multimedia analysis, among which multi-label image classification is a very challenging task with respect to the large demand for human annotation of multi-label samples. In this paper, we propose a multi-view multi-label active learning strategy, which integrates the mechanism of active learning and multi ...
Changsheng Xu, Songde Ma
exaly   +2 more sources

Latent Semantic Aware Multi-View Multi-Label Classification

Proceedings of the AAAI Conference on Artificial Intelligence, 2018
For real-world applications, data are often associated with multiple labels and represented with multiple views. Most existing multi-label learning methods do not sufficiently consider the complementary information among multiple views, leading to unsatisfying performance.
Changqing Zhang 0002, Ziwei Yu, Qinghua Hu, Pengfei Zhu 0001, Xinwang Liu 0002, Xiaobo Wang 0001   +5 more
openaire   +2 more sources

Multi-View Metric Learning for Multi-Label Image Classification

2019 IEEE International Conference on Image Processing (ICIP), 2019
Multi-label image classification is a very challenging task, where data are often associated with multiple labels and represented with multiple views. In this paper, we propose a novel multi-view distance metric learning approach to dealing with the multi-label image classification problem.
Mengying Zhang, Changsheng Li, Xiangfeng Wang 0001   +2 more
openaire   +2 more sources

A Multi-view Active Learning Approach for the Hierarchical Multi-label Classification of Research Papers

2021
In this paper, we focus on the hierarchical multi-label classification task of scientific papers, which consists in assigning to a paper the set of relevant classes, which are organized in a hierarchy. The difficulty of manually constructing sufficient labeled datasets renders challenging the automatic classification task of research papers according ...
Abir Masmoudi 0002, Hatem Bellaaj, Mohamed Jmaiel   +2 more
openaire   +2 more sources