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- Aagaard, Marianne, 1985-
(författare)
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Naivt tro att EU kan stoppa bedrägerierna
- 2024
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Annan publikation (populärvet., debatt m.m.)abstract
- Vi kan inte förlita oss på att EU löser våra problem, utan måste själva skapa regler som gynnar trygga betalningssätt.
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- Abani, Chris
(författare)
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Meet Chris Abani
- 2010
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Annan publikation (populärvet., debatt m.m.)abstract
- The poet and author Chris Abani talks about alienation and poetry.
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- Abid, Nosheen, et al.
(författare)
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UCL: Unsupervised Curriculum Learning for Image Classification
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Annan publikation (populärvet., debatt m.m.)abstract
- In many real-world applications of computer vision complex domains, such as medical diagnostics and document analysis, the lack of labeled data often limits the effectiveness of traditional deep learning models. This study addresses these challenges by enhancing Unsupervised Curriculum Learning (UCL), a deep learning framework that automatically discovers meaningful patterns without the need for labeled data. Originally designed for remote sensing imagery, UCL has been expanded in this work to improve classification performance in a variety of domain-specific applications. UCL integrates a convolutional neural network, clustering algorithms, and selection techniques to classify images unsupervised. We introduce key improvements, such as spectral clustering, outlier detection, and dimensionality reduction, to boost the framework’s accuracy. Experimental results demonstrate significant performance gains, with F1-scores increasing from 68% to 94% on a three-class subset of the CIFAR-10 dataset and from 68% to 75% on a five-class subset. The updated UCL also achieved F1-scores of 85% in medical diagnosis, 82% in scene recognition, and 62% in historical document classification. These findings underscore the potential of UCL in complex real-world applications and point to areas where further advancements are needed to maximize its utility across diverse fields.
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