Lecture
Kernels: Nonlinear Transformations
Description
This lecture covers the transition from linear to nonlinear problems in machine learning, introducing the concept of kernels to simplify data representation. It explains how kernels make data linearly separable in feature spaces, explores popular kernel functions like Gaussian and polynomial kernels, and discusses the properties and effects of different kernels. Practical exercises demonstrate isolines and the impact of kernel parameters on data separation.
This video is available exclusively on Mediaspace for a restricted audience. Please log in to MediaSpace to access it if you have the necessary permissions.
Watch on MediaspaceOfficial source
About this result
This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.