CT-based deep multi-label learning prediction model for outcome in patients with oropharyngeal squamous cell carcinoma.
Sign Up to like & getrecommendations! Published in 2023 at "Medical physics"
DOI: 10.1002/mp.16465
Abstract: BACKGROUND Personalized treatment is increasingly required for oropharyngeal squamous cell carcinoma (OPSCC) patients due to emerging new cancer subtypes and treatment options. Outcome prediction model can help identify low or high-risk patients who may be… read more here.
Keywords: outcome; test; prediction; multi label ... See more keywords
Partial label learning via low-rank representation and label propagation
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DOI: 10.1007/s00500-019-04269-9
Abstract: In partial label learning, each training instance is assigned with a set of candidate labels, among which only one is correct. An intuitive strategy to learn from such ambiguous data is disambiguation. Existing methods following… read more here.
Keywords: label; rank representation; low rank; via low ... See more keywords
Learning safe multi-label prediction for weakly labeled data
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DOI: 10.1007/s10994-017-5675-z
Abstract: In this paper we study multi-label learning with weakly labeled data, i.e., labels of training examples are incomplete, which commonly occurs in real applications, e.g., image classification, document categorization. This setting includes, e.g., (i) semi-supervised… read more here.
Keywords: label; weakly labeled; multi label; label learning ... See more keywords
Alignment Based Kernel Selection for Multi-Label Learning
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DOI: 10.1007/s11063-018-9863-z
Abstract: Kernel based methods are increasingly being used for data modeling because of their conceptual simplicity and outstanding performance on many learning tasks. And kernel alignment, which is usually employed to select particular kernel for a… read more here.
Keywords: label; multi label; label learning; kernel alignment ... See more keywords
Multi-label Learning with Missing Labels Using Mixed Dependency Graphs
Sign Up to like & getrecommendations! Published in 2018 at "International Journal of Computer Vision"
DOI: 10.1007/s11263-018-1085-3
Abstract: This work focuses on the problem of multi-label learning with missing labels (MLML), which aims to label each test instance with multiple class labels given training instances that have an incomplete/partial set of these labels… read more here.
Keywords: label; multi label; missing labels; graph ... See more keywords
Incremental Multi-Label Learning with Active Queries
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DOI: 10.1007/s11390-020-9994-3
Abstract: In multi-label learning, it is rather expensive to label instances since they are simultaneously associated with multiple labels. Therefore, active learning, which reduces the labeling cost by actively querying the labels of the most valuable… read more here.
Keywords: classification model; label; multi label; label learning ... See more keywords
A Graph-based Semi-supervised Multi-label Learning Method Based on Label Correlation Consistency
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DOI: 10.1007/s12559-021-09912-y
Abstract: Multi-label learning deals with the problem which each data example can be represented by an instance and associated with a set of labels, i.e., every example can be classified into multiple classes simultaneously. Most of… read more here.
Keywords: label; semi supervised; multi label; label learning ... See more keywords
Regularized partial least squares for multi-label learning
Sign Up to like & getrecommendations! Published in 2018 at "International Journal of Machine Learning and Cybernetics"
DOI: 10.1007/s13042-016-0500-8
Abstract: AbstractIn reality, data objects often belong to several different categories simultaneously, which are semantically correlated to each other. Multi-label learning can handle and extract useful information from such kind of data effectively. Since it has… read more here.
Keywords: least squares; multi label; regularized partial; label learning ... See more keywords
A multi-label learning framework for predicting antibiotic resistance genes via dual-view modeling
Sign Up to like & getrecommendations! Published in 2022 at "Briefings in bioinformatics"
DOI: 10.1093/bib/bbac052
Abstract: The increasing prevalence of antibiotic resistance has become a global health crisis. For the purpose of safety regulation, it is of high importance to identify antibiotic resistance genes (ARGs) in bacteria. Although culture-based methods can… read more here.
Keywords: antibiotic resistance; multi label; framework; label learning ... See more keywords
Expede Herculem: Learning Multi Labels From Single Label
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DOI: 10.1109/access.2018.2876014
Abstract: Although there has been a lot of research in multi-label learning task, little attention has been paid on the weak label problem, in which only a subset of labels has been assigned to each instance… read more here.
Keywords: label; multi; weak label; expede herculem ... See more keywords
Imprecise Deep Forest for Partial Label Learning
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DOI: 10.1109/access.2020.3042838
Abstract: In partial label (PL) learning, each instance corresponds to a set of candidate labels, among which only one is valid. The objective of PL learning is to obtain a multi-class classifier from the training instances.… read more here.
Keywords: label; training; deep forest; ecoc algorithm ... See more keywords