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Near-optimal sample compression for near...

Gottlieb, Lee-Ad...

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Near-optimal sample compression for nearest neighbors by Gottlieb, Lee-Ad ( Author )

Content Provider	Lemonjar Open Access
Publisher	Australian National University
Publication Date	01-09-2023
Summary	We present the first sample compression algorithm for nearest neighbors with non-trivial performance guarantees. We complement these guarantees by demonstrating almost matching hardness lower bounds, which show that our bound is nearly optimal. Our result yields new insight into margin-based nearest neighbor classification in metric spaces and allows us to significantly sharpen and simplify existing bounds. Some encouraging empirical results are also presented.
ISBN	-
Item Type	Article
File Type	pdf
File Size	29.34 KB
Language	English
Subjects	-
Unlimited	MYR 0.01
Total read	-
URL	http://arxiv.org/abs/1404.3368

Citation

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AMA 11th EditionReferences

Gottlieb, Lee-Ad. Near-optimal sample compression for nearest neighbors. Australian National University. September 01, 2023. Accessed November 10, 2024. https://online.provdatabase.com/cite/23333

APA 7th EditionReferences

Gottlieb, Lee-Ad. (2023). Near-optimal sample compression for nearest neighbors. Australian National University. https://online.provdatabase.com/cite/23333

Chicago 17th EditionReferences

Gottlieb, Lee-Ad. Near-optimal sample compression for nearest neighbors. Australian National University. 2023. https://online.provdatabase.com/cite/23333

HarvardReferences

Gottlieb, Lee-Ad. (2023). Near-optimal sample compression for nearest neighbors. [Online]. Australian National University. Available from: https://online.provdatabase.com/cite/23333. (Accessed 10 November 2024).

Harvard AustraliaReferences

Gottlieb, Lee-Ad, 2023, Near-optimal sample compression for nearest neighbors, Australian National University, viewed 10 November 2024, <https://online.provdatabase.com/cite/23333>

MLA 9th EditionWorks Cited

Gottlieb, Lee-Ad. Near-optimal sample compression for nearest neighbors. Australian National University, 2023. PROV Database, https://online.provdatabase.com/cite/23333.

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