Results 51 to 60 of about 601,753 (327)
Distributed Semi-Supervised Metric Learning
IEEE Access, 2016 Over the last decade, many pairwise-constraint-based metric learning algorithms have been developed to automatically learn application-specific metrics from data under similarity/dissimilarity data-pair constraints (weak labels).
Pengcheng Shen, Xin Du, Chunguang Li
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Benchmarking the Semi-Supervised Naïve Bayes Classifier [PDF]
, 2015 Semi-supervised learning involves constructing predictive models with both labelled and unlabelled training data. The need for semi-supervised learning is driven by the fact that unlabelled data are often easy and cheap to obtain, whereas labelling data ...
Bagnall, Anthony +2 more
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South African Computer Journal, 2020 Semi-supervised learning is a potential solution for improving training data in low-resourced abusive language detection contexts such as South African abusive language detection on Twitter.
Oluwafemi Oriola, Eduan Kotzé
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Semi-Supervised Apprenticeship Learning
, 2012 In apprenticeship learning we aim to learn a good policy by observing the behavior of an expert or a set of experts. In particular, we consider the case where the expert acts so as to maximize an unknown reward function defined as a linear combination of a set of state features.
Michal Vaľko +2 more
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Semi-supervised few-shot learning approach for plant diseases recognition
Plant Methods, 2021 Background Learning from a few samples to automatically recognize the plant leaf diseases is an attractive and promising study to protect the agricultural yield and quality.
Yang Li, Xuewei Chao
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Semi-supervised Vocabulary-Informed Learning [PDF]
2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 Despite significant progress in object categorization, in recent years, a number of important challenges remain, mainly, ability to learn from limited labeled data and ability to recognize object classes within large, potentially open, set of labels. Zero-shot learning is one way of addressing these challenges, but it has only been shown to work with ...
Yanwei Fu, Leonid Sigal
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Generative Adversarial Training for Supervised and Semi-supervised Learning
Frontiers in Neurorobotics, 2022 Neural networks have played critical roles in many research fields. The recently proposed adversarial training (AT) can improve the generalization ability of neural networks by adding intentional perturbations in the training process, but sometimes still
Xianmin Wang +7 more
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Lautum Regularization for Semi-supervised Transfer Learning [PDF]
, 2020 Transfer learning is a very important tool in deep learning as it allows propagating information from one "source dataset" to another "target dataset", especially in the case of a small number of training examples in the latter.
Giryes, Raja +2 more
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Lγ-PageRank for semi-supervised learning [PDF]
Applied Network Science, 2019 PageRank for Semi-Supervised Learning has shown to leverage data structures and limited tagged examples to yield meaningful classification. Despite successes, classification performance can still be improved, particularly in cases of fuzzy graphs or unbalanced labeled data.
Bautista, Esteban +2 more
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An Efficient Approach to Select Instances in Self-Training and Co-Training Semi-Supervised Methods
IEEE Access, 2022 Semi-supervised learning is a machine learning approach that integrates supervised and unsupervised learning mechanisms. In this learning, most of labels in the training set are unknown, while there is a small part of data that has known labels. The semi-
Karliane Medeiros Ovidio Vale +3 more
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