SVM can only produce linear boundaries between classes by default, which not enough for most machine learning applications. In order to get nonlinear boundaries, you have to pre-apply a nonlinear transformation to the data. The kernel trick allows you to bypass the need for specifying this nonlinear transformation explicitly. Instead, you specify a "kernel" function that directly describes how each points relate to each other. Kernels are much more fun to work with and come with important computational benefits.
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Credit:
🐍 Manim and Python : github.com/3b1b/manim
🐵 Blender3D: www.blender.org/
🗒️ Emacs: www.gnu.org/software/emacs/
This video would not have been possible without the help of Gökçe Dayanıklı.
Негізгі бет Ғылым және технология The Kernel Trick in Support Vector Machine (SVM)
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