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HGAT: Heterogeneous Graph Attention Networks for Semi-supervised Short Text Classification
ACM Trans. Inf. Syst., 2021Short text classification has been widely explored in news tagging to provide more efficient search strategies and more effective search results for information retrieval. However, most existing studies, concentrating on long text classification, deliver
Tianchi Yang +5 more
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2020 Third International Conference on Smart Systems and Inventive Technology (ICSSIT), 2020
Recent developments in analytical technologies helped in developing applications for real-time problems faced by industries. These applications are often found to consume more time in the training phase.
Ganapathi Raju +6 more
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Recent developments in analytical technologies helped in developing applications for real-time problems faced by industries. These applications are often found to consume more time in the training phase.
Ganapathi Raju +6 more
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Non Supervised Classification Tools Adapted to Supervised Classification
1987Let X be some set of individuals. We consider the following two mappings: $$\begin{array}{*{20}{c}} {R:X \to {R^p}} \\ {\Omega :X \to \left\{ {{\omega _1}, \ldots ,{\omega _n}} \right\}} \end{array}$$ For an individual x, x ∈ X, R(x) is its representation (R p being the feature-space) and Ω(x) is its class.
R. Fages +3 more
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Convex Multiview Semi-Supervised Classification
IEEE Transactions on Image Processing, 2017In many practical applications, there are a great number of unlabeled samples available, while labeling them is a costly and tedious process. Therefore, how to utilize unlabeled samples to assist digging out potential information about the problem is very important. In this paper, we study a multiclass semi-supervised classification task in the context
Feiping Nie, Jing Li, Xuelong Li
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Safety-Aware Semi-Supervised Classification
IEEE Transactions on Neural Networks and Learning Systems, 2013Though semi-supervised classification learning has attracted great attention over past decades, semi-supervised classification methods may show worse performance than their supervised counterparts in some cases, consequently reducing their confidence in real applications.
Yunyun, Wang, Songcan, Chen
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Supervised radar signal classification
2016 International Joint Conference on Neural Networks (IJCNN), 2016This work investigates radar signal classification and source identification using three classification models: Neural Networks (NN), Support Vector Machines (SVM) and Random Forests (RF). The available large dataset consists of pulse train characteristics such as signal frequencies, type of modulation, pulse repetition intervals, scanning type, scan ...
Ivan Jordanov +2 more
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Semi-supervised classification trees
Journal of Intelligent Information Systems, 2017In many real-life problems, obtaining labelled data can be a very expensive and laborious task, while unlabeled data can be abundant. The availability of labeled data can seriously limit the performance of supervised learning methods. Here, we propose a semi-supervised classification tree induction algorithm that can exploit both the labelled and ...
Jurica Levatić +3 more
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Semi-Supervised Classification of Graph Convolutional Networks with Laplacian Rank Constraints
Neural Processing Letters, 2021Haiqi Zhang +3 more
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Optimization approaches to Supervised Classification
European Journal of Operational Research, 2017zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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