Support Vector Machines (SVM)
📘 Artificial Intelligence & Machine Learning Basics
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📅 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:
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Maximizes the margin between different classes
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Can handle linear and non-linear data using kernel functions (linear, polynomial, RBF)
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Effective in high-dimensional spaces
Applications:
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Face detection
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Text and email classification
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Bioinformatics (e.g., cancer detection)
Advantages:
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Works well with complex and high-dimensional data
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Robust against overfitting
Limitations:
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Computationally intensive for large datasets
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Choosing the right kernel can be challenging
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