Definition:
A supervised learning algorithm used for classification and regression, which finds the best boundary (hyperplane) that separates classes.
Key Points:
Maximizes the margin between different classes
Can handle linear and non-linear data using kernel functions (linear, polynomial, RBF)
Effective in high-dimensional spaces
Applications:
Face detection
Text and email classification
Bioinformatics (e.g., cancer detection)
Advantages:
Works well with complex and high-dimensional data
Robust against overfitting
Limitations:
Computationally intensive for large datasets
Choosing the right kernel can be challenging
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