Results 151 to 160 of about 637,915 (182)

Multi-label Quadruplet Dictionary Learning

2020
The explosion of the label space degrades the performance of the classic multi-class learning models. Label space dimension reduction (LSDR) is developed to reduce the dimension of the label space by learning a latent representation of both the feature space and label space.
Jiayu Zheng, Wencheng Zhu, Pengfei Zhu
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Multi-label Learning

2018
The defining characteristic of multi-label as opposed to single-label data is that each instance can belong to several classes at once. The multi-label classification task is to predict all relevant labels of a target instance. This chapter presents and experimentally evaluates our FRONEC method, the Fuzzy Rough NEighbourhood Consensus.
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Unconstrained Multimodal Multi-Label Learning

IEEE Transactions on Multimedia, 2015
Multimodal learning has been mostly studied by assuming that multiple label assignments are independent of each other and all the modalities are available. In this paper, we consider a more general problem where the labels contain dependency relationships and some modalities are likely to be missing.
Yan Huang, Wei Wang, Liang Wang
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Dual Set Multi-Label Learning

Proceedings of the AAAI Conference on Artificial Intelligence, 2018
In this paper, we propose a new learning framework named dual set multi-label learning, where there are two sets of labels, and an object has one and only one positive label in each set. Compared to general multi-label learning, the exclusive relationship among labels within the same set, and the pairwise inter-set label relationship ...
Chong Liu, Peng Zhao, Sheng-Jun Huang, Yuan Jiang, Zhi-Hua Zhou   +4 more
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Multi-Label Learning from Crowds

IEEE Transactions on Knowledge and Data Engineering, 2019
We consider multi-label crowdsourcing learning in two scenarios. In the first scenario, we aim at inferring instances’ groundtruth given the crowds’ annotations. We propose two approaches NAM/RAM (Neighborhood/Relevance Aware Multi-label crowdsourcing) modeling the crowds’ expertise and label correlations from different perspectives.
Shao-Yuan Li, Yuan Jiang, Nitesh V. Chawla, Zhi-Hua Zhou   +3 more
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Tabular multi-label learning

Tabular data is essential in data science and machine learning, supporting a wide range of real-world applications across various industries, such as finance, healthcare, marketing, and customer segmentation. Traditionally, classification tasks in tabular data involve assigning a single label to each instance, which works well for binary or multiclass ...
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Multi-label learning by exploiting label dependency

Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining, 2010
In multi-label learning, each training example is associated with a set of labels and the task is to predict the proper label set for the unseen example. Due to the tremendous (exponential) number of possible label sets, the task of learning from multi-label examples is rather challenging. Therefore, the key to successful multi-label learning is how to
Zhang, M., Zhang, K.
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Multi-Label Supervised Contrastive Learning

Proceedings of the AAAI Conference on Artificial Intelligence
Multi-label classification is an arduous problem given the complication in label correlation. Whilst sharing a common goal with contrastive learning in utilizing correlations for representation learning, how to better leverage label information remains challenging. Previous endeavors include extracting label-level presentations or mapping labels to an
Pingyue Zhang, Mengyue Wu
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The technological landscape and applications of single-cell multi-omics

Nature Reviews Molecular Cell Biology, 2023
Zhiliang Bai, Rahul Satija, Rong Fan
exaly  

Multi-label Learning

2016
Zhi-Hua Zhou, Min-Ling Zhang
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