Machine learning

Support Vector Machine (Classification)

The Support Vector Machine, introduced by Corinna Cortes and Vladimir Vapnik in 1995, is a classifier that finds the optimal separating hyperplane between classes in a high-dimensional space. It chooses the boundary that leaves the widest possible margin to the nearest training points, which makes its decisions robust on new data.

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Cortes, C. & Vapnik, V. (1995). Support-Vector Networks. Machine Learning, 20, 273–297. DOI: 10.1007/BF00994018 ↗

Referenced by

Active learning Support vector machine Capsule Network CNN Image Classification Convolutional Neural Network DBSCAN Decision Tree Graph Neural Network K-Nearest Neighbors Kernel PCA Linear Discriminant Analysis (Classification)Naive Bayes Random Forest Self-supervised Support Vector Machine Semi-supervised Support Vector Machine Stacking Support Vector Regression Vision Transformer XGBoost

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