Results 21 to 30 of about 8,068,470 (297)
LMGAN: Linguistically Informed Semi-Supervised GAN with Multiple Generators
Sensors, 2022 Semi-supervised learning is one of the active research topics these days. There is a trial that solves semi-supervised text classification with a generative adversarial network (GAN).
Whanhee Cho, Yongsuk Choi
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Weakly Semi Supervised learning based Mixture Model With Two-Level Constraints
Proceedings of the International Florida Artificial Intelligence Research Society Conference, 2021 We propose a new weakly supervised approach for classification and clustering based on mixture models. Our approach integrates multi-level pairwise group and class constraints between samples to learn the underlying group structure of the data and ...
Adama Nouboukpo, Mohand Saïd Allili
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Semi-supervised Learning for Anomalous Trajectory Detection [PDF]
, 2008 A novel learning framework is proposed for anomalous behaviour detection in a video surveillance scenario, so that a classifier which distinguishes between normal and anomalous behaviour patterns can be incrementally trained with the assistance of a ...
Fisher, Bob +3 more
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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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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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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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