Results 51 to 60 of about 637,915 (182)

Minimal learning machine for multi-label learning

Machine Learning
Abstract Distance-based supervised method, the minimal learning machine, constructs a predictive model from data by learning a mapping between input and output distance matrices. In this paper, we propose new methods and evaluate how their core component, the distance mapping, can be adapted to multi-label learning.
Joonas Hämäläinen, Antoine Hubermont, Amauri Souza, César L. C. Mattos, João P. P. Gomes, Tommi Kärkkäinen   +5 more
openaire   +2 more sources

Object-Aware Self-Supervised Multi-Label Learning

2022 IEEE International Conference on Image Processing (ICIP), 2022
Accepted by IEEE International Conference on Image Processing (ICIP 2022)
Kaixin, Xu, Liyang, Liu, Ziyuan, Zhao, Zeng, Zeng, Veeravalli, Bharadwaj   +4 more
openaire   +2 more sources

Label Distribution Learning

, 2016
Although multi-label learning can deal with many problems with label ambiguity, it does not fit some real applications well where the overall distribution of the importance of the labels matters.
Geng, Xin
core   +1 more source

LP-MLTSVM: Laplacian Multi-Label Twin Support Vector Machine for Semi-Supervised Classification

IEEE Access, 2022
In the machine learning jargon, multi-label classification refers to a task where multiple mutually non-exclusive class labels are assigned to a single instance. Generally, the lack of sufficient labeled training data demanded by a classification task is
Farhad Gharebaghi, Ali Amiri
doaj   +1 more source

A Novel Progressive Multi-label Classifier for Classincremental Data

, 2016
In this paper, a progressive learning algorithm for multi-label classification to learn new labels while retaining the knowledge of previous labels is designed.
Dave, Mihika, Er, Meng Joo, Tapiawala, Sahil, Venkatesan, Rajasekar   +3 more
core   +1 more source

Learning Common and Label-Specific Features for Multi-Label Classification With Missing Labels

IEEE Access
Multi-label learning is a subfield of machine learning that addresses the issue of each instance belonging to numerous class labels at the same time. However, in some real applications, we can only receive a partial set of labels for each instance due to
Runxin Li, Zexian Ouyang, Zhenhong Shang, Lianyin Jia, Xiaowu Li   +4 more
doaj   +1 more source

Intuitionistic Fuzzy-Based Three-Way Label Enhancement for Multi-Label Classification

Mathematics, 2022
Multi-label classification deals with the determination of instance-label associations for unseen instances. Although many margin-based approaches are delicately developed, the uncertainty classifications for those with smaller separation margins remain ...
Tianna Zhao, Yuanjian Zhang, Duoqian Miao   +2 more
doaj   +1 more source

Deep active learning for multi label text classification

Scientific Reports
Given a set of labels, multi-label text classification (MLTC) aims to assign multiple relevant labels for a text. Recently, deep learning models get inspiring results in MLTC.
Qunbo Wang, Hangu Zhang, Wentao Zhang, Lin Dai, Yu Liang, Haobin Shi   +5 more
doaj   +1 more source

Multi-Label Attribute Selection of Arrhythmia for Electrocardiogram Signals with Fusion Learning

Bioengineering, 2022
There are three primary challenges in the automatic diagnosis of arrhythmias by electrocardiogram (ECG): the significant variation among individual patients, the multiple pathologies in the ECG signal and the high cost in annotating clinical ECG with the
Jie Yang, Jinfeng Li, Kun Lan, Anruo Wei, Han Wang, Shigao Huang, Simon Fong   +6 more
doaj   +1 more source

Multi-Label Zero-Shot Learning with Structured Knowledge Graphs

, 2018
In this paper, we propose a novel deep learning architecture for multi-label zero-shot learning (ML-ZSL), which is able to predict multiple unseen class labels for each input instance. Inspired by the way humans utilize semantic knowledge between objects
Fang, Wei, Lee, Chung-Wei, Wang, Yu-Chiang Frank, Yeh, Chih-Kuan   +3 more
core   +1 more source