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Near Extensions and Alignment of Data in R(superscript)n
book

Near Extensions and Alignment of Data in R(superscript)n

by Steven B. Damelin
November 2023
Intermediate to advanced content levelIntermediate to advanced
192 pages
3h 29m
English
Wiley
Content preview from Near Extensions and Alignment of Data in R(superscript)n

References

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  3. [3] J. H. Ann, S. B. Damelin, P. Bigeleisen, Medical image segmentation using modified Mumford segmentation methods, Ultrasound-Guided Regional Anesthesia and Pain Medicine, eds P. Bigeleisen, Chapter 40, Birkhauser, 2009.
  4. [4] M. Artetxe, G. Labaka and E. Agirre, A robust self-learning method for fully unsupervised cross-lingual mappings of word embeddings, Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 789–798, Melbourne, Australia, July 2018. Association for Computational Linguistics.
  5. [5] Y. Bai, B. Schmitzer, M. Thorpe and S. Kolouri, Sliced optimal transport, arXiv:2212.08049.
  6. [6] Y. Bai, T. Huy, S. B. Damelin and S. Kolouri, Noisy point cloud registration via optimal partial transport, preprint.
  7. [7] B. Bajnok, S. B. Damelin, J. Li and G. Mullen, A constructive method of scattering points on d-dimensional spheres using finite fields, Computing, 68(2002), pp. 97–109.
  8. [8] E. Begelfor and M. Werman, Affine invariance revisited, 2006 IEEE Computer Society Conference on Computer Vision and Pattern recognition.
  9. [9] E. Bengelfor and M. Werman, The world is not always ...
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Publisher Resources

ISBN: 9781394196777Purchase Link