Machine Learning - Support Vector Machines Part 3: The Radial (RBF) Kernel (Part 3 of 3)

Support Vector Machines use kernel functions to do all the hard work and this StatQuest dives deep into one of the most popular: The Radial (RBF) Kernel. We talk about the parameter values, how they calculate high-dimensional coordinates and then we'll figure out, step-by-step, how the Radial Kernel works in infinite dimensions. NOTE: This StatQuest assumes you already know about... Support Vector Machines: https://youtu.be/efR1C6CvhmE Cross Validation: https://youtu.be/fSytzGwwBVw The Polynomial Kernel: https://youtu.be/Toet3EiSFcM ALSO NOTE: This StatQuest is based on... 1) The description of Kernel Functions, and associated concepts on pages 352 to 353 of the Introduction to Statistical Learning in R: http://faculty.marshall.usc.edu/gareth-james/ISL/ 2) The derivation of the of the infinite dot product is based on Matthew Bernstein's notes: http://pages.cs.wisc.edu/~matthewb/pages/notes/pdf/svms/RBFKernel.pdf For a complete index of all the StatQuest videos, check out: https://statquest.org/video-index/ If you'd like to support StatQuest, please consider... Patreon: https://www.patreon.com/statquest ...or... YouTube Membership: https://www.youtube.com/channel/UCtYLUTtgS3k1Fg4y5tAhLbw/join ...buying one of my books, a study guide, a t-shirt or hoodie, or a song from the StatQuest store... https://statquest.org/statquest-store/ ...or just donating to StatQuest! https://www.paypal.me/statquest Lastly, if you want to keep up with me as I research and create new StatQuests, follow me on twitter: https://twitter.com/joshuastarmer #statquest #SVM #RBF