Support Vector Machine

"Support Vector Machine" is a descriptor in the National Library of Medicine's controlled vocabulary thesaurus, MeSH (Medical Subject Headings). Descriptors are arranged in a hierarchical structure, which enables searching at various levels of specificity.

MeSH information

Definition | Details | More General Concepts | Related Concepts | More Specific Concepts

SUPERVISED MACHINE LEARNING algorithm which learns to assign labels to objects from a set of training examples. Examples are learning to recognize fraudulent credit card activity by examining hundreds or thousands of fraudulent and non-fraudulent credit card activity, or learning to make disease diagnosis or prognosis based on automatic classification of microarray gene expression profiles drawn from hundreds or thousands of samples.

Descriptor ID	D060388
MeSH Number(s)	G17.035.250.500.500.500 L01.224.050.375.530.500.500
Concept/Terms	Support Vector Machine Support Vector Machine Machine, Support Vector Machines, Support Vector Support Vector Machines Vector Machine, Support Vector Machines, Support Support Vector Network Support Vector Network Network, Support Vector Networks, Support Vector Support Vector Networks Vector Network, Support Vector Networks, Support

Below are MeSH descriptors whose meaning is more general than "Support Vector Machine".

Biological Sciences [G]
Mathematical Concepts [G17]
Algorithms [G17.035]
Artificial Intelligence [G17.035.250]
Machine Learning [G17.035.250.500]
Supervised Machine Learning [G17.035.250.500.500]
Support Vector Machine [G17.035.250.500.500.500]
Information Science [L]
Information Science [L01]
Computing Methodologies [L01.224]
Algorithms [L01.224.050]
Artificial Intelligence [L01.224.050.375]
Machine Learning [L01.224.050.375.530]
Supervised Machine Learning [L01.224.050.375.530.500]
Support Vector Machine [L01.224.050.375.530.500.500]

Below are MeSH descriptors whose meaning is related to "Support Vector Machine".

Below are MeSH descriptors whose meaning is more specific than "Support Vector Machine".

publications

Timeline | Most Recent

This graph shows the total number of publications written about "Support Vector Machine" by people in this website by year, and whether "Support Vector Machine" was a major or minor topic of these publications.

Bar chart showing 19 publications over 12 distinct years, with a maximum of 3 publications in 2014 and 2021

To see the data from this visualization as text, click here.

Year	Major Topic	Minor Topic	Total
2011	1	0	1
2012	0	2	2
2013	0	1	1
2014	2	1	3
2017	0	1	1
2018	1	0	1
2019	0	2	2
2020	0	2	2
2021	0	3	3
2022	0	1	1
2023	0	1	1
2024	0	1	1

Below are the most recent publications written about "Support Vector Machine" by people in Profiles.

Secemsky EA, Aronow HD, Kwolek CJ, Meissner M, Muck PE, Parikh SA, Winokur RS, George JC, Salazar G, Murphy EH, Costantino MM, Zhou W, Li J, Lookstein R, Desai KR. Intravascular Ultrasound Use in Peripheral Arterial and Deep Venous Interventions: Multidisciplinary Expert Opinion From SCAI/AVF/AVLS/SIR/SVM/SVS. J Vasc Interv Radiol. 2024 03; 35(3):335-348.

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Hammoud B, El Zini J, Awad M, Sweid A, Tjoumakaris S, Jabbour P. Predicting incomplete occlusion of intracranial aneurysms treated with flow diverters using machine learning models. J Neurosurg. 2024 06 01; 140(6):1716-1725.

View in: PubMed
Le H, Peng B, Uy J, Carrillo D, Zhang Y, Aevermann BD, Scheuermann RH. Machine learning for cell type classification from single nucleus RNA sequencing data. PLoS One. 2022; 17(9):e0275070.

View in: PubMed
Thakran S, Gupta RK, Singh A. Characterization of breast tumors using machine learning based upon multiparametric magnetic resonance imaging features. NMR Biomed. 2022 05; 35(5):e4665.

View in: PubMed
Creager MA, Hamburg NM, Calligaro KD, Casanegra AI, Freeman R, Gordon PA, Gornik HL, Kim ESH, Leeper NJ, Merli GJ, Niazi K, Olin JW, Quiroz R, Rrapo Kaso E, Wasan S, Waxler AR, White CJ, White Solaru K, Williams MS. 2021 ACC/AHA/SVM/ACP Advanced Training Statement on Vascular Medicine (Revision of the 2004 ACC/ACP/SCAI/SVMB/SVS Clinical Competence Statement on Vascular Medicine and Catheter-Based Peripheral Vascular Interventions). Circ Cardiovasc Interv. 2021 02; 14(2):e000079.

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Creager MA, Hamburg NM, Calligaro KD, Casanegra AI, Freeman R, Gordon PA, Gornik HL, Kim ESH, Leeper NJ, Merli GJ, Niazi K, Olin JW, Quiroz R, Kaso ER, Wasan S, Waxler AR, White CJ, Solaru KW, Williams MS. 2021 ACC/AHA/SVM/ACP Advanced Training Statement on Vascular Medicine (Revision of the 2004 ACC/ACP/SCAI/SVMB/SVS Clinical Competence Statement on Vascular Medicine and Catheter-Based Peripheral Vascular Interventions). Vasc Med. 2021 Feb; 26(1):91-112.

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Goryawala M, Roy B, Gupta RK, Maudsley AA. T1-weighted and T2-weighted Subtraction MR Images for Glioma Visualization and Grading. J Neuroimaging. 2021 01; 31(1):124-131.

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Tozlu C, Edwards D, Boes A, Labar D, Tsagaris KZ, Silverstein J, Pepper Lane H, Sabuncu MR, Liu C, Kuceyeski A. Machine Learning Methods Predict Individual Upper-Limb Motor Impairment Following Therapy in Chronic Stroke. Neurorehabil Neural Repair. 2020 05; 34(5):428-439.

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Garcea FE, Stoll H, Buxbaum LJ. Reduced competition between tool action neighbors in left hemisphere stroke. Cortex. 2019 11; 120:269-283.

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MacCormick IJC, Williams BM, Zheng Y, Li K, Al-Bander B, Czanner S, Cheeseman R, Willoughby CE, Brown EN, Spaeth GL, Czanner G. Accurate, fast, data efficient and interpretable glaucoma diagnosis with automated spatial analysis of the whole cup to disc profile. PLoS One. 2019; 14(1):e0209409.

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