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Classification of imbalanced land-use/land-cover data using variational semi-supervised learning
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1007/978-3-642-02326-2_38openalex-snapshot
Classifying Remote Sensing Data with Support Vector Machines and Imbalanced Training Data
inferredcites →https://doi.org/10.1016/j.apgeog.2010.11.006openalex-snapshot
Comparison of methods for land-use classification incorporating remote sensing and GIS inputs
inferredcites →https://doi.org/10.1016/j.neunet.2013.01.021openalex-snapshot
A neural network algorithm for semi-supervised node label learning from unbalanced data
inferredcites →https://doi.org/10.1016/j.rse.2014.12.014openalex-snapshot
Improvement and expansion of the Fmask algorithm: cloud, cloud shadow, and snow detection for Landsats 4–7, 8, and Sentinel 2 images
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Selecting and interpreting measures of thematic classification accuracy
inferredcites →https://doi.org/10.1016/s0167-8655(97)00109-8openalex-snapshot
Classification of imbalanced remote-sensing data by neural networks
inferredcites →https://doi.org/10.1080/10106048709354126openalex-snapshot
Introduction to remote sensing
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On Information and Sufficiency
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SMOTE: Synthetic Minority Over-sampling Technique
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AutoResearch Lite: a reproducible paper-inspired experiment runner (Adam mechanism reproduction and convergence-speed comparison)
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ADADELTA: An Adaptive Learning Rate Method
inferredcites →https://doi.org/10.48550/arxiv.1312.6114openalex-snapshot
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inferredcites →https://doi.org/10.48550/arxiv.1406.5298openalex-snapshot
Semi-Supervised Learning with Deep Generative Models
inferredcites →https://doi.org/10.48550/arxiv.1412.6980openalex-snapshot
Adam: A Method for Stochastic Optimization
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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