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.
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
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MeSH Number(s) |
G17.035.250.500.500.500 L01.224.050.375.530.500.500
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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
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Below are MeSH descriptors whose meaning is more general than "Support Vector Machine".
Below are MeSH descriptors whose meaning is more specific than "Support Vector Machine".
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.
To see the data from this visualization as text, click here.
Year | Major Topic | Minor Topic | Total |
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2011 | 0 | 1 | 1 | 2013 | 2 | 1 | 3 | 2014 | 0 | 2 | 2 | 2016 | 1 | 2 | 3 | 2017 | 1 | 1 | 2 | 2018 | 0 | 1 | 1 | 2019 | 0 | 2 | 2 | 2020 | 0 | 1 | 1 | 2021 | 0 | 2 | 2 | 2022 | 1 | 6 | 7 |
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Below are the most recent publications written about "Support Vector Machine" by people in Profiles.
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Saulsberry L, Bhargava A, Zeng S, Gibbons JB, Brannan C, Lauderdale DS, Gibbons RD. The social vulnerability metric (SVM) as a new tool for public health. Health Serv Res. 2023 08; 58(4):873-881.
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Rodrigo H, Beukes EW, Andersson G, Manchaiah V. Predicting the Outcomes of Internet-Based Cognitive Behavioral Therapy for Tinnitus: Applications of Artificial Neural Network and Support Vector Machine. Am J Audiol. 2022 Dec 05; 31(4):1167-1177.
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Mehrpour O, Saeedi F, Hoyte C, Goss F, Shirazi FM. Utility of support vector machine and decision tree to identify the prognosis of metformin poisoning in the United States: analysis of National Poisoning Data System. BMC Pharmacol Toxicol. 2022 07 13; 23(1):49.
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van 't Hof SR, Van Oudenhove L, Janssen E, Klein S, Reddan MC, Kragel PA, Stark R, Wager TD. The Brain Activation-Based Sexual Image Classifier (BASIC): A Sensitive and Specific fMRI Activity Pattern for Sexual Image Processing. Cereb Cortex. 2022 07 12; 32(14):3014-3030.
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Li WX, Tong X, Yang PP, Zheng Y, Liang JH, Li GH, Liu D, Guan DG, Dai SX. Screening of antibacterial compounds with novel structure from the FDA approved drugs using machine learning methods. Aging (Albany NY). 2022 02 12; 14(3):1448-1472.
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Lee AM, Hu J, Xu Y, Abraham AG, Xiao R, Coresh J, Rebholz C, Chen J, Rhee EP, Feldman HI, Ramachandran VS, Kimmel PL, Warady BA, Furth SL, Denburg MR. Using Machine Learning to Identify Metabolomic Signatures of Pediatric Chronic Kidney Disease Etiology. J Am Soc Nephrol. 2022 02; 33(2):375-386.
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Potcoava MC, Futia GL, Gibson EA, Schlaepfer IR. Raman Microscopy Techniques to Study Lipid Droplet Composition in Cancer Cells. Methods Mol Biol. 2022; 2413:193-209.
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Gillenwater LA, Helmi S, Stene E, Pratte KA, Zhuang Y, Schuyler RP, Lange L, Castaldi PJ, Hersh CP, Banaei-Kashani F, Bowler RP, Kechris KJ. Multi-omics subtyping pipeline for chronic obstructive pulmonary disease. PLoS One. 2021; 16(8):e0255337.
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Li MD, Deng F, Chang K, Kalpathy-Cramer J, Huang AJ. Automated Radiology-Arthroscopy Correlation of Knee Meniscal Tears Using Natural Language Processing Algorithms. Acad Radiol. 2022 04; 29(4):479-487.
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Sharma AM, Kim ES, Kadian-Dodov D, Armstrong E. The 2020 SVM/SVU Consensus Statement for the Interpretation of Peripheral Arterial and Venous Doppler Waveforms: An interview with SVM members of the Writing Committee. Vasc Med. 2021 Feb; 26(1):117-118.
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