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Paper

A simple and reliable instance selection for fast training support vector machine: Valid Border Recognition

by Independent / Community 0015f809776f5ee6ad42c1597152c3c114d02efb

Free2AITools Nexus Index

64.7

S: Semantic 50

Query-time baseline · scored live at search

A: Authority 74

P: Popularity 49

R: Recency 100

Q: Quality 65

Tech Context

Vital Performance —

Support vector machines (SVMs) are powerful statistical learning tools, but their application to large datasets can cause time-consuming training complexity. To address this issue, various instance selection (IS) approaches have been proposed, which choose a small fraction of critical instances and screen out others before training. However, existing methods have not been able to balance accuracy and efficiency well. Some methods miss critical instances, while others use complicated selection...

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Semantic Scholar 7 Citations

Paper Information Summary
Entity Passport
Registry ID	0015f809776f5ee6ad42c1597152c3c114d02efb
License	ArXiv
Provider	semantic_scholar

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BibTeX

@misc{0015f809776f5ee6ad42c1597152c3c114d02efb,
  author = {Unknown},
  title = {A simple and reliable instance selection for fast training support vector machine: Valid Border Recognition Paper},
  year = {2026},
  howpublished = {\url{https://api.semanticscholar.org/0015f809776f5ee6ad42c1597152c3c114d02efb}},
  note = {Accessed via Free2AITools.}
}

APA Style

Unknown. (2026). A simple and reliable instance selection for fast training support vector machine: Valid Border Recognition [Paper]. Free2AITools. https://api.semanticscholar.org/0015f809776f5ee6ad42c1597152c3c114d02efb

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⚖️ Free2AITools Nexus Index V2.0

Methodology How FNI works

Semantic (S) 50

Query-time baseline · scored live at search

Authority (A) 74

Popularity (P) 49

Recency (R) 100

Quality (Q) 65

💬 Index Insight

FNI V2.0 for A simple and reliable instance selection for fast training support vector machine: Valid Border Recognition: Authority (A:74), Popularity (P:49), Recency (R:100), Quality (Q:65). Semantic (S) is a query-time baseline scored live at search.

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📝 Executive Summary

"Support vector machines (SVMs) are powerful statistical learning tools, but their application to large datasets can cause time-consuming training complexity. To address this issue, various instance selection (IS) approaches have been proposed, which choose a small fraction of critical instances and screen out others before training. However, existing methods have not been able to balance accuracy and efficiency well. Some methods miss critical instances, while others use complicated selection..."

❝ Cite Node

@article{Unknown2026A,
  title={A simple and reliable instance selection for fast training support vector machine: Valid Border Recognition},
  author={},
  note={Indexed by Free2AITools},
  year={2026}
}

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📊 Research Signals

📈7CitationsSemantic Scholar

🏛️74AuthorityFNI pillar

⏱️100RecencyFNI pillar

✅65QualityFNI pillar

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🏷️ Research Topics

vector databases

📦Data Source: semantic_scholar

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source: semantic_scholar
author: Unknown
license: ArXiv
tags: paper, research, academic

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architecture: null
params billions: null
context length: null
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downloads: 0
stars: null
forks: null
citations: 7

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