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Unsupervised learning of shape manifolds [PDF]
Procedings of the British Machine Vision Conference 2007, 2007 Classical shape analysis methods use principal component analysis to reduce the dimensionality of shape spaces. The basic assumption behind these methods is that the subspace corresponding to the major modes of variation for a particular class of shapes ...
Bhalerao, Abhir +5 more
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Fairness in Unsupervised Learning
Proceedings of the 29th ACM International Conference on Information & Knowledge Management, 2020 Data in digital form is expanding at an exponential rate, far outpacing any chance of getting any significant fraction labelled manually. This has resulted in heightened research emphasis on unsupervised learning, learning in the absence of labels.
Deepak P +8 more
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Unsupervised Learning of Morphology [PDF]
Computational Linguistics, 2021 This article surveys work on Unsupervised Learning of Morphology. We define Unsupervised Learning of Morphology as the problem of inducing a description (of some kind, even if only morpheme-segmentation) of how orthographic words are built up given only raw text data of a language.
Harald Hammarström, Lars Borin
doaj +7 more sources
American Journal of Orthodontics and Dentofacial Orthopedics, 2023 This work was supported by the Flemish Government under the “Onderzoeksprogramma Artifici€ele Intelligentie (AI) Vlaanderen ...
Dirk Valkenborg +3 more
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Unsupervised learning of generative topic saliency for person re-identification [PDF]
, 2014 (c) 2014. The copyright of this document resides with its authors. It may be distributed unchanged freely in print or electronic forms.© 2014. The copyright of this document resides with its authors.
Wang, H +5 more
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Unsupervised learning and generalization [PDF]
Proceedings of International Conference on Neural Networks (ICNN'96), 2002 The concept of generalization is defined for a general class of unsupervised learning machines. The generalization error is a straightforward extension of the corresponding concept for supervised learning, and may be estimated empirically using a test set or by statistical means-in close analogy with supervised learning.
Lars Kai Hansen, Jan Larsen
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Unsupervised Tokenization Learning
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, 2022 In the presented study, we discover that the so-called "transition freedom" metric appears superior for unsupervised tokenization purposes in comparison to statistical metrics such as mutual information and conditional probability, providing F-measure scores in range from 0.71 to 1.0 across explored multilingual corpora.
Anton Kolonin, Vignav Ramesh
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Unsupervised Learning for Parametric Optimization [PDF]
IEEE Communications Letters, 2021 This work was supported by the European Research Council under the H2020 Framework Programme/ERC grant agreement 694974, by the Maria de Maeztu Units of Excellence Programme (MDM-2015-0502) as well as by MINECO’s Projects RTI2018-102112 and RTI2018-101040, and by the ICREA Academia program.
Rasoul Nikbakht +2 more
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Unsupervised Learning of Edges [PDF]
2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 Camera ready version for CVPR ...
Yin Li 0003 +3 more
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Unsupervised two-class & multi-class support vector machines for abnormal traffic characterization. [PDF]
, 2009 Although measurement-based real-time traffic classification has received considerable research attention, the timing constraints imposed by the high accuracy requirements and the learning phase of the algorithms employed still remain a challenge. In this
Kim, Hyun-chul +7 more
core +3 more sources