Publication2020
Hyperspectral Image Classification Using Stochastic Gradient Descent Based Support Vector Machine
The record
- Type
- Publication
- Date
- 1 January 2020
- Level
- National
- Journal
- Learning and Analytics in Intelligent Systems
- Volume
- 10
- Pages
- 78-84
- Published
- 1 January 2020
- DOI
- 10.1007/978-3-030-39033-4_8
- Scopus ID
- 85179864371
- ISSN
- 26623447
- Authors
- 4
- Open access
- No
- Citations
- 0
- International co-authorship
- No
- Co-author countries
- IN
Classification
Every label below carries the reasoning it was assigned on, the confidence band it was assigned in, and what produced it. The rubric is published.
UN Sustainable Development Goals
9.5 Enhance scientific research, upgrade technological capabilities, and increase researchers and R&D spending
Support Vector Machine optimization with stochastic gradient descent for hyperspectral remote sensing classification advances machine learning research.
- Moderate confidence
- The alignment is plausible and defensible but rests on an assumption a reasonable reviewer could decline. Published, and surfaced in the review queue for a human to confirm or override.
- Judged
- Judged against the published classification rubric, under the rubric and prompt versions recorded with the run that produced it.
- Produced by
- claude-haiku-4-5-20251001, under kortex-publication-v1
Viksit Bharat @2047
vb-3.2 Digital technology and artificial intelligence
Hyperspectral image classification technology supports digital technology and artificial intelligence research development.
- Produced by
- claude-haiku-4-5-20251001, under kortex-publication-v1
Each label above carries the instrument and the version that produced it. How each classification is reached.
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- 1
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Citing this record
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KIIT Deemed to be University. “Hyperspectral Image Classification Using Stochastic Gradient Descent Based Support Vector Machine”. Kortex.