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Supervised Classification Of induction Motors faults [PDF]
E3S Web of Conferences, 2022 Currently, the environmental challenges have been considered as a strategic issue for most industrial companies around the world, threatening their sustainability and profit; This leads to taking the environmental dimensions seriously and preserving ...
El bouisfi Radouane +2 more
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Semi-supervised Learning Method Based on Automated Mixed Sample Data Augmentation Techniques [PDF]
Jisuanji kexue, 2022 Consistency-based semi-supervised learning methods typically use simple data augmentation methods to achieve consistent predictions for both original inputs and perturbed inputs.The effectiveness of this approach is difficult to be guaranteed when the ...
XU Hua-jie, CHEN Yu, YANG Yang, QIN Yuan-zhuo
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Supervised Classification Problems–Taxonomy of Dimensions and Notation for Problems Identification
IEEE Access, 2021 The paper proposes a taxonomy for categorizing the main features of the supervised learning classification problems and a notation for the identification of the supervised learning classification problem categories.
Ireneusz Czarnowski, Piotr Jedrzejowicz
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Remote Sensing, 2023 Few-shot hyperspectral classification is a challenging problem that involves obtaining effective spatial–spectral features in an unsupervised or semi-supervised manner.
Chunyu Li, Rong Cai, Junchuan Yu
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COMPARISON OF SEVERAL REMOTE SENSING IMAGE CLASSIFICATION METHODS BASED ON ENVI [PDF]
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2020 With the development of remote sensing technology and the increasing accuracy of remote sensing images, research on the accuracy of remote sensing classification is becoming more and more important.
X. C. Li +6 more
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Projected Estimators for Robust Semi-supervised Classification [PDF]
, 2016 For semi-supervised techniques to be applied safely in practice we at least want methods to outperform their supervised counterparts. We study this question for classification using the well-known quadratic surrogate loss function.
Krijthe, Jesse H., Loog, Marco
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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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SEMI-SUPERVISED MARGINAL FISHER ANALYSIS FOR HYPERSPECTRAL IMAGE CLASSIFICATION [PDF]
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2012 The problem of learning with both labeled and unlabeled examples arises frequently in Hyperspectral image (HSI) classification. While marginal Fisher analysis is a supervised method, which cannot be directly applied for Semi-supervised classification ...
H. Huang, J. Liu, Y. Pan
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Partial least squares discriminant analysis: A dimensionality reduction method to classify hyperspectral data [PDF]
, 2018 The recent development of more sophisticated spectroscopic methods allows acqui- sition of high dimensional datasets from which valuable information may be extracted using multivariate statistical analyses, such as dimensionality reduction and automatic ...
Bellincontro, Andrea +2 more
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ReliaMatch: Semi-Supervised Classification with Reliable Match
Applied Sciences, 2023 Deep learning has been widely used in various tasks such as computer vision, natural language processing, predictive analysis, and recommendation systems in the past decade.
Tao Jiang +4 more
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