Results 21 to 30 of about 637,915 (182)
A Multi-Objective Multi-Label Feature Selection Algorithm Based on Shapley Value
Entropy, 2021 Multi-label learning is dedicated to learning functions so that each sample is labeled with a true label set. With the increase of data knowledge, the feature dimensionality is increasing.
Hongbin Dong, Jing Sun, Xiaohang Sun
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Large-Scale Multi-Label Learning with Incomplete Label Assignments [PDF]
, 2014 Multi-label learning deals with the classification problems where each instance can be assigned with multiple labels simultaneously. Conventional multi-label learning approaches mainly focus on exploiting label correlations.
Fan, Wei +6 more
core +2 more sources
Multi-instance multi-label learning
Artificial Intelligence, 2012 64 pages, 10 figures; Artificial Intelligence ...
Zhou, Zhi-Hua +3 more
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Fast Multi-Instance Multi-Label Learning [PDF]
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2014 In multi-instance multi-label learning (MIML), one object is represented by multiple instances and simultaneously associated with multiple labels. Existing MIML approaches have been found useful in many applications; however, most of them can only handle moderate-sized data. To efficiently handle large data sets, we propose the MIMLfast
Sheng-Jun Huang, Wei Gao, Zhi-Hua Zhou
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IEEE Access, 2019 Multi-label learning deals with problems in which each instance is associated with a set of labels. Most multi-label learning algorithms ignore the potential distribution differences between the training domain and the test domain in the instance space ...
Siyu Jiang +6 more
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A hybrid algorithm for Bayesian network structure learning with application to multi-label learning [PDF]
, 2014 We present a novel hybrid algorithm for Bayesian network structure learning, called H2PC. It first reconstructs the skeleton of a Bayesian network and then performs a Bayesian-scoring greedy hill-climbing search to orient the edges.
Aussem, Alex +2 more
core +6 more sources
Multi-Label Learning from Single Positive Labels [PDF]
2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 Predicting all applicable labels for a given image is known as multi-label classification. Compared to the standard multi-class case (where each image has only one label), it is considerably more challenging to annotate training data for multi-label classification.
Cole, Elijah +5 more
openaire +5 more sources
A Unified Framework for Graph-Based Multi-View Partial Multi-Label Learning
IEEE Access, 2023 Multi-view partial multi-label learning (MVPML) is a fundenmental problem where each sample is linked to multiple kinds of features and candidate labels, including ground-truth and noise labels.
Jiazheng Yuan +3 more
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Zero‐shot multi‐label learning via label factorisation
IET Computer Vision, 2019 This study considers the zero‐shot learning problem under the multi‐label setting where each test sample is associated with multiple labels that are unseen in training data.
Hang Shao +3 more
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Study on Multi-label Image Classification Based on Sample Distribution Loss [PDF]
Jisuanji kexue, 2022 Different from the data distribution in general image classification scenarios,in the scenario of multi label image classification,the sample number distribution among different label categories is unbalanced,and a small number of head categories often ...
ZHU Xu-dong, XIONG Yun
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