Exploring Kernel Regression
Exploring Kernel Regression reveals several interesting facts.
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- SVM can only produce linear boundaries between classes by default, which not enough for most machine learning applications.
- The
In-Depth Information on Kernel Regression
This video is part of the Udacity course "Supervised Learning". Watch the full course at https://www.udacity.com/course/ud726. Notes: https://users.cs.duke.edu/~cynthia/CourseNotes/LeastSquaresAndFriends.pdf. I cover two methods for nonparametric regression: the binned scatterplot and the Nadaraya-Watson Some parametric methods, like polynomial
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