Support Vector Machine (SVM)
Last Updated: July 29, 2026 | By Mihail Sebastian | AI Dictionary
A supervised algorithm that separates classes with the widest possible margin, defined by the few borderline points (support vectors) closest to the boundary.
What is a Support Vector Machine?
A support vector machine (SVM) is a supervised learning algorithm that classifies data by finding the boundary separating the classes with the widest possible margin. A variant handles regression, but classification is its home ground.
The name comes from the support vectors: the few data points closest to the boundary, which alone determine where it sits. Every other point could move or vanish without changing the model.
How an SVM Works
When the classes are linearly separable, the SVM draws the hyperplane (a line in two dimensions, a plane in three) that leaves the largest gap to the nearest points on either side. A wide margin is the point of the exercise: boundaries that barely squeeze between classes generalize badly to new data.
When no straight boundary works, the kernel trick maps the data into a higher-dimensional space where one does. A kernel method computes similarities in that space without ever constructing it, which keeps the trick affordable.
SVMs hold up well when features outnumber examples, which made them a longtime standard for text classification and for bioinformatics tasks like gene-expression analysis.
Example of an SVM
A spam filter represents each email as a point whose coordinates are word frequencies: how often it says “free”, “invoice”, “unsubscribe”. Spam and legitimate mail form two clouds in that space.
The SVM finds the boundary with the widest margin between the clouds. The support vectors are the borderline messages: a real order confirmation full of sales language, a spam email disguised as a personal note. Those hard cases, not the thousands of obvious ones, define the filter.
A new email arrives, becomes a point, and lands on one side of the boundary. Which side is the verdict.
Related AI terms: K-Nearest Neighbors · Decision Tree · Kernel Method · Supervised Learning
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Mihail Sebastian — Writes about AI governance, regulation, and the technology behind them. Placeholder bio — replace with a real credential line. About