Support Vector Machines are one of the most mysterious methods in Machine Learning. This StatQuest sweeps away the mystery to let know how they work.
Part 2: The Polynomial Kernel: [ Ссылка ]
Part 3: The Radial (RBF) Kernel: [ Ссылка ]
NOTE: This StatQuest assumes you already know about...
The bias/variance tradeoff: [ Ссылка ]
Cross Validation: [ Ссылка ]
ALSO NOTE: This StatQuest is based on description of Support Vector Machines, and associated concepts, found on pages 337 to 354 of the Introduction to Statistical Learning in R: [ Ссылка ]
I also found this blogpost helpful for understanding the Kernel Trick: [ Ссылка ]
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0:00 Awesome song and introduction
0:40 Basic concepts and Maximal Margin Classifiers
4:35 Soft Margins (allowing misclassifications)
6:46 Soft Margin and Support Vector Classifiers
12:23 Intuition behind Support Vector Machines
15:25 The polynomial kernel function
17:30 The radial basis function (RBF) kernel
18:32 The kernel trick
19:31 Summary of concepts
#statquest #SVM
