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Supervised Classification, discriminant analysis

Freakonometrics, 2015
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HopRank: Self-Supervised LLM Preference-Tuning on Graphs for Few-Shot Node Classification [PDF]

Ziqing Wang, Kaize Ding
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Safety-Aware Semi-Supervised Classification

IEEE Transactions on Neural Networks and Learning Systems, 2013
Though 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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Non Supervised Classification Tools Adapted to Supervised Classification

1987
Let 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, M. Terrenoire, D. Tounissoux, A. Zighed   +3 more
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Supervised Classification

2023
Juan J. Cuadrado-Gallego, Yuri Demchenko
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Convex Multiview Semi-Supervised Classification

IEEE Transactions on Image Processing, 2017
In 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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Supervised radar signal classification

2016 International Joint Conference on Neural Networks (IJCNN), 2016
This 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, Nedyalko Petrov, Alessio Petrozziello   +2 more
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Semi-supervised classification trees

Journal of Intelligent Information Systems, 2017
In 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, Michelangelo Ceci, Dragi Kocev, Sašo Džeroski   +3 more
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Optimization approaches to Supervised Classification

European Journal of Operational Research, 2017
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Self-Supervised Cloud Classification

Artificial Intelligence for the Earth Systems
Abstract Low-level marine clouds play a pivotal role in Earth’s weather and climate through their interactions with radiation, heat and moisture transport, and the hydrological cycle. These interactions depend on a range of dynamical and microphysical processes that result in a broad diversity of cloud types and spatial structures, and a comprehensive ...
Andrew Geiss, Matthew W. Christensen, Adam C. Varble, Tianle Yuan, Hua Song   +4 more
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