Support Vector Machines (SVM)

Support Vector Machines (SVM) are supervised learning algorithms primarily used for classification, but they can also handle regression tasks. SVM aims to find the optimal hyperplane that separates data points of different classes with the maximum margin.

The decision boundary is defined by:

w · x + b = 0

For non-linearly separable data, SVM uses the kernel trick to project data into higher-dimensional space, making it easier to find a separating hyperplane.

SVM is highly effective in text classification, bioinformatics, and image recognition.