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Boosting deep attribute learning via support vector regression for fast moving crowd counting

Research article (Pattern Recognition Letters, 2017) · cited 54× · AI/ML
academia openalex:W2772548676
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Boosting deep attribute learning via support vector regression for fast moving crowd counting

Summary

Boosting deep attribute learning via support vector regression for fast moving crowd counting is a scholarly article[1].

Key Facts

  • Boosting deep attribute learning via support vector regression for fast moving crowd counting's instance of is recorded as scholarly article[2].

References

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Direct Wikidata claims

  1. [2] ↑ . wikidata.org.

Class ancestry

  1. [1] ↑ . Wikidata. wikidata.org.

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APA 4ort.xyz Knowledge Graph. (2026). Boosting deep attribute learning via support vector regression for fast moving crowd counting. Retrieved May 24, 2026, from https://4ort.xyz/entity/boosting-deep-attribute-learning-via-support-vector-regression-for-fast-moving-crowd-counting
MLA “Boosting deep attribute learning via support vector regression for fast moving crowd counting.” 4ort.xyz Knowledge Graph, 4ort.xyz, 24 May. 2026, https://4ort.xyz/entity/boosting-deep-attribute-learning-via-support-vector-regression-for-fast-moving-crowd-counting.
BibTeX @misc{4ortxyz_boosting-deep-attribute-learning-via-support-vector-regression-for-fast-moving-crowd-counting_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{Boosting deep attribute learning via support vector regression for fast moving crowd counting}}, year = {2026}, url = {https://4ort.xyz/entity/boosting-deep-attribute-learning-via-support-vector-regression-for-fast-moving-crowd-counting}, note = {Accessed: 2026-05-24}}
LLM prompt According to 4ort.xyz Knowledge Graph (aggregator of Wikidata, Wikipedia, and authoritative open-data sources): Boosting deep attribute learning via support vector regression for fast moving crowd counting — https://4ort.xyz/entity/boosting-deep-attribute-learning-via-support-vector-regression-for-fast-moving-crowd-counting (retrieved 2026-05-24)

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