Results 21 to 30 of about 8,398,730 (304)
Semi-supervised Learning Algorithm Based on Maximum Margin and Manifold Hypothesis [PDF]
Jisuanji kexueSemi-supervised learning is a weakly supervised learning pattern between supervised learning and unsupervised lear-ning.It combines a small number of labeled instances with a large number of unlabeled instances to build a model during the process of ...
DAI Wei, CHAI Jing, LIU Yajiao
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Feature ranking for semi-supervised learning
Machine Learning, 2022 AbstractThe data used for analysis are becoming increasingly complex along several directions: high dimensionality, number of examples and availability of labels for the examples. This poses a variety of challenges for the existing machine learning methods, related to analyzing datasets with a large number of examples that are described in a high ...
Matej Petkovic +2 more
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Semi-Supervised Learning for Image Classification using Compact Networks in the BioMedical Context [PDF]
, 2022 Background and objectives: The development of mobile and on the edge appli-cations that embed deep convolutional neural models has the potential to revolutionisebiomedicine.
Díaz-Pinto, Andrés +5 more
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A review on graph-based semi-supervised learning methods for hyperspectral image classification
Egyptian Journal of Remote Sensing and Space Sciences, 2020 In this article, a comprehensive review of the state-of-art graph-based learning methods for classification of the hyperspectral images (HSI) is provided, including a spectral information based graph semi-supervised classification and a spectral-spatial ...
Shrutika S. Sawant, Manoharan Prabukumar
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Tracking-based semi-supervised learning [PDF]
The International Journal of Robotics Research, 2011 We consider a semi-supervised approach to the problem of track classification in dense three-dimensional range data. This problem involves the classification of objects that have been segmented and tracked without the use of a class-specific tracker. This paper is an extended version of our previous work.
Alex Teichman, Sebastian Thrun
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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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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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Semi-supervised learning with regularized Laplacian [PDF]
Optimization Methods and Software, 2016 We study a semi-supervised learning method based on the similarity graph and RegularizedLaplacian. We give convenient optimization formulation of the Regularized Laplacian method and establishits various properties. In particular, we show that the kernel of the methodcan be interpreted in terms of discrete and continuous time random walks and possesses
Konstantin Avrachenkov +2 more
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Semi‐supervised learning dehazing algorithm based on the OSV model
IET Image Processing, 2023 Despite the great progress that has been made in the task of single image dehazing, the results of the existing models in restoring image edge and texture information are still challenging.
Lijun Zhu +5 more
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Quantum semi-supervised kernel learning
Quantum Machine Intelligence, 2021 Quantum computing leverages quantum effects to build algorithms that are faster then their classical variants. In machine learning, for a given model architecture, the speed of training the model is typically determined by the size of the training dataset.
Seyran Saeedi +2 more
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