svm

SVM - Support Vector Machines

Description: SVMs find the hyperplane that best separates different classes by maximizing the margin between them.

Key Points:

• Effective in high-dimensional spaces.
• Works well for both linear and non-linear classification using kernel trick.
• Sensitive to the choice of kernel and regularization parameter.

Applications: Image classification, text categorization, bioinformatics.