"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
|
| 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".
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 |
|---|
| 2011 | 1 | 0 | 1 |
| 2012 | 1 | 3 | 4 |
| 2013 | 1 | 0 | 1 |
| 2014 | 0 | 2 | 2 |
| 2015 | 0 | 1 | 1 |
| 2016 | 0 | 2 | 2 |
| 2018 | 0 | 3 | 3 |
| 2019 | 2 | 2 | 4 |
| 2021 | 0 | 1 | 1 |
| 2022 | 0 | 1 | 1 |
| 2023 | 0 | 1 | 1 |
To return to the timeline,
click here.
Below are the most recent publications written about "Support Vector Machine" by people in Profiles.
-
Optical coherence tomography confirms non-malignant pigmented lesions in phacomatosis pigmentokeratotica using a support vector machine learning algorithm. Skin Res Technol. 2023 Jun; 29(6):e13377.
-
The social vulnerability metric (SVM) as a new tool for public health. Health Serv Res. 2023 08; 58(4):873-881.
-
Odor identity can be extracted from the reciprocal connectivity between olfactory bulb and piriform cortex in humans. Neuroimage. 2021 08 15; 237:118130.
-
Characterizing functional regional homogeneity (ReHo) as a B-SNIP psychosis biomarker using traditional and machine learning approaches. Schizophr Res. 2020 01; 215:430-438.
-
Comparison of machine learning classifiers for differentiation of grade 1 from higher gradings in meningioma: A multicenter radiomics study. Magn Reson Imaging. 2019 11; 63:244-249.
-
A machine-learning-based prediction model of fistula formation after interstitial brachytherapy for locally advanced gynecological malignancies. Brachytherapy. 2019 Jul - Aug; 18(4):530-538.
-
Multi-institutional Clinical Tool for Predicting High-risk Lesions on 3Tesla Multiparametric Prostate Magnetic Resonance Imaging. Eur Urol Oncol. 2019 05; 2(3):257-264.
-
Perinodular and Intranodular Radiomic Features on Lung CT Images Distinguish Adenocarcinomas from Granulomas. Radiology. 2019 03; 290(3):783-792.
-
Shape Features of the Lesion Habitat to Differentiate Brain Tumor Progression from Pseudoprogression on Routine Multiparametric MRI: A Multisite Study. AJNR Am J Neuroradiol. 2018 12; 39(12):2187-2193.
-
Big data: More than big data sets. Surgery. 2018 10; 164(4):640-642.