Support vector machine

In machine learning, support vector machines (SVMs, also support vector networks) are supervised max-margin models with associated learning algorithms that analyze data for classification and regression analysis. Developed at AT&T Bell Laboratories, SVMs are one of the most studied models, being based on statistical learning frameworks of VC theory proposed by Vapnik (1982, 1995) and Chervonenkis (1974).

Support vector machine - Wikipedia Jump to content From Wikipedia, the free encyclopedia Set of methods for supervised statistical learning Part of a series on Machine learning and data mining Paradigms Supervised learning Unsupervised learning Semi-supervised learning Self-supervised learning Reinforcement learning Meta-learning Online learning Batch learning Curriculum learning Rule-based learning Neuro-symbolic AI Neuromorphic engineering Quantum machine learning Problems Classification Generative modeling Regression Clustering Dimensionality reduction Density estimation Anomaly detection D

saved by

• Janabel Xia
• Lydia Nottingham

related reading

• Support vector machine - Wikipediaen.wikipedia.org
• Hyperparameter optimization - Wikipediaen.wikipedia.org
• CS231n Deep Learning for Computer Visioncs231n.github.io
• Deep Learning using Linear Support Vector Machinesarxiv.org
• Neural networks and deep learningneuralnetworksanddeeplearning.com
• deeplearningbook.org/contents/ml.htmldeeplearningbook.org
• The Little Book of Deep Learningfleuret.org
• Self-Labeled Techniques for Semi-Supervised Learning: Taxonomy, Software and Empirical Study | Soft Computing and Intelligent Information Systemssci2s.ugr.es
• Machine learning - Wikipediaen.wikipedia.org
• Top 10 Machine Learning Algorithms in 2026 - Analytics Vidhyaanalyticsvidhya.com
• Statistical learning theory - Wikipediaen.wikipedia.org
• Self-organizing map - Wikipediaen.wikipedia.org