Reference Summary: SVM can only produce linear boundaries between classes by default, which not enough for most machine learning applications. Some parametric methods, like polynomial regression and Support Vector Machines stand out as being very versatile.
The Kernel Trick -
SVM can only produce linear boundaries between classes by default, which not enough for most machine learning applications. Some parametric methods, like polynomial regression and Support Vector Machines stand out as being very versatile. Each video is based on the corresponding subsection in my notes posted at ...
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- SVM can only produce linear boundaries between classes by default, which not enough for most machine learning applications.
- Some parametric methods, like polynomial regression and Support Vector Machines stand out as being very versatile.
- Each video is based on the corresponding subsection in my notes posted at ...
- This video is part of the Udacity course "Introduction to Computer Vision".
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