Methods › General › Non-Parametric Regression › SVM

Support Vector Machine

SVM

1,823 papers tagged archive 2025-07-28

archive 2025-07-28 Description, source and code snippet are the archive's method entry.

A Support Vector Machine, or SVM, is a non-parametric supervised learning model. For non-linear classification and regression, they utilise the kernel trick to map inputs to high-dimensional feature spaces. SVMs construct a hyper-plane or set of hyper-planes in a high or infinite dimensional space, which can be used for classification, regression or other tasks. Intuitively, a good separation is achieved by the hyper-plane that has the largest distance to the nearest training data points of any class (so-called functional margin), since in general the larger the margin the lower the generalization error of the classifier. The figure to the right shows the decision function for a linearly separable problem, with three samples on the margin boundaries, called “support vectors”.

Source: scikit-learn

Papers archive 2025-07-28

30 shown of 1,823, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.

Tasks archive 2025-07-28

20 shown of 630 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.

TaskPapers
General Classification385
Classification266
BIG-bench Machine Learning153
feature selection90
Binary Classification76
Sentiment Analysis70
regression69
Image Classification55
Clustering51
image-classification51
Text Classification49
EEG48
Transfer Learning45
Deep Learning43
Object Detection43
Anomaly Detection42
text-classification42
object-detection41
Diagnostic40
Prediction40

Usage over time archive 2025-07-28

Papers per year tagged with SVM: 2005 to 2025, peak 208 208 0 2005: 1 paper 2005 2006: 0 papers 2007: 5 papers 2007 2008: 6 papers 2009: 6 papers 2009 2010: 12 papers 2011: 1 paper 2011 2012: 14 papers 2013: 53 papers 2013 2014: 73 papers 2015: 111 papers 2015 2016: 108 papers 2017: 166 papers 2017 2018: 141 papers 2019: 190 papers 2019 2020: 208 papers 2021: 180 papers 2021 2022: 148 papers 2023: 149 papers 2023 2024: 182 papers 2025: 69 papers 2025
Papers per year the archive tags with this method, by the paper's archive date (1,823 dated). Bars are counts, not a trend claim.

Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).

Categories archive 2025-07-28

Non-Parametric RegressionNon-Parametric Classification

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