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Transductive Multi-label Zero-shot Learning [PDF]
Proceedings of the British Machine Vision Conference 2014, 2014 Zero-shot learning has received increasing interest as a means to alleviate the often prohibitive expense of annotating training data for large scale recognition problems.
Fu, Yanwei +4 more
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Multi-Label Learning with Label Enhancement [PDF]
2018 IEEE International Conference on Data Mining (ICDM), 2019 The task of multi-label learning is to predict a set of relevant labels for the unseen instance. Traditional multi-label learning algorithms treat each class label as a logical indicator of whether the corresponding label is relevant or irrelevant to the
Geng, Xin, Shao, Ruifeng, Xu, Ning
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Collaboration based Multi-Label Learning
Proceedings of the AAAI Conference on Artificial Intelligence, 2019 It is well-known that exploiting label correlations is crucially important to multi-label learning. Most of the existing approaches take label correlations as prior knowledge, which may not correctly characterize the real relationships among labels ...
An, Bo, Feng, Lei, He, Shuo
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Local Rademacher Complexity for Multi-Label Learning [PDF]
IEEE Transactions on Image Processing, 2016 © 1992-2012 IEEE. We analyze the local Rademacher complexity of empirical risk minimization-based multi-label learning algorithms, and in doing so propose a new algorithm for multi-label learning.
Liu, T, Tao, D, Xu, C
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Deep Extreme Multi-label Learning [PDF]
Proceedings of the 2018 ACM on International Conference on Multimedia Retrieval, 2018 Extreme multi-label learning (XML) or classification has been a practical and important problem since the boom of big data. The main challenge lies in the exponential label space which involves $2^L$ possible label sets especially when the label ...
Wang, Xiangfeng +3 more
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Multi-Graph Multi-Label Learning Based on Entropy [PDF]
Entropy, 2018 Recently, Multi-Graph Learning was proposed as the extension of Multi-Instance Learning and has achieved some successes. However, to the best of our knowledge, currently, there is no study working on Multi-Graph Multi-Label Learning, where each object is
Zixuan Zhu, Yuhai Zhao
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Learning Interpretable Rules for Multi-label Classification [PDF]
, 2018 Multi-label classification (MLC) is a supervised learning problem in which, contrary to standard multiclass classification, an instance can be associated with several class labels simultaneously.
A Gabriel +43 more
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Federated Multi-Label Learning (FMLL): Innovative Method for Classification Tasks in Animal Science [PDF]
AnimalsFederated learning is a collaborative machine learning paradigm where multiple parties jointly train a predictive model while keeping their data. On the other hand, multi-label learning deals with classification tasks where instances may simultaneously ...
Bita Ghasemkhani +5 more
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Review on Multi-lable Classification [PDF]
Jisuanji kexue yu tansuo, 2023 Multi-label classification refers to the classification problem where multiple labels may coexist in a single sample. It has been widely applied in fields such as text classification, image classification, music and video classification.
LI Dongmei, YANG Yu, MENG Xianghao, ZHANG Xiaoping, SONG Chao, ZHAO Yufeng
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Fast Multi-label Learning [PDF]
Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, 2021 Embedding approaches have become one of the most pervasive techniques for multi-label classification. However, the training process of embedding methods usually involves a complex quadratic or semidefinite programming problem, or the model may even involve an NP-hard problem. Thus, such methods are prohibitive on large-scale applications.
Gong, Xiuwen, Yuan, Dong, Bao, Wei
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