Support Vector Machines (SVM)

📘 Artificial Intelligence & Machine Learning Basics 👁 92 views 📅 Nov 05, 2025
⏱ Estimated reading time: 1 min

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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