Support Vector Machines

34 min
Block 11 — Classification Algorithms
Objective
Define the separating hyperplane, margin, and support vectors rigorously; understand why margin maximization is grounded in generalization theory; master the role of parameter C in soft-margin classification; explain the kernel trick and gamma's effect; recognize when feature scaling is mandatory; know regression via SVR, multiclass strategies, and imbalanced data remedies; learn why SVMs have receded yet remain valuable.
Estimated duration
90 minutes
Prerequisites
Chapters 009, 023, 030, 031, 032, 035, and 040
Associated quizzes
041.1-... through 041.8-...

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This lesson is part of the “Classification Algorithms” module

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