A supervised machine learning algorithm used for classification and regression that finds the optimal hyperplane to separate data into distinct classes.
Key Characteristics:
Goal: Maximize the margin between data points of different classes.
Support Vectors: The critical data points closest to the decision boundary that influence the position and orientation of the hyperplane.
Linear or Nonlinear: Can handle linear separation directly or use kernel functions (like RBF, polynomial) to handle nonlinear relationships by transforming data into higher-dimensional spaces.
Use Cases:
Text classification
Image recognition
Bioinformatics
Fraud detection
In addition to performing linear classification, SVMs can efficiently perform non-linear classification using the kernel trick, representing the data only through a set of pairwise similarity comparisons between the original data points using a kernel function, which transforms them into coordinates in a higher-dimensional feature space. Thus, SVMs use the kernel trick to implicitly map their inputs into high-dimensional feature spaces, where linear classification can be performed.[3] Being max-margin models, SVMs are resilient to noisy data (e.g., misclassified examples). SVMs can also be used for regression tasks, where the objective becomes -sensitive.
