Kernel Ridge Regression: Equivalent Formulations and Representer Theorem

In course

Et Lorem incididunt aute voluptate aute. Sit sunt minim quis quis enim ipsum voluptate in incididunt. Do non pariatur sunt aute exercitation excepteur ut nisi labore quis. Do tempor culpa do deserunt eu elit adipisicing quis nisi sint qui ea ut. Veniam eiusmod irure quis aute nostrud.

Description

This lecture covers Kernel Ridge Regression, presenting equivalent formulations for Ridge regression and the Representer Theorem. It explains the usefulness of alternative forms, the Kernel trick, and building new kernels from old ones. The lecture also delves into the Mercer's condition and predicting with kernels.

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 Mediaspace

Instructors (2)

adipisicing elit quis cupidatat

Anim ipsum tempor ut irure adipisicing cupidatat ut. Sunt officia aliquip ad est ut dolor ea sit Lorem qui laboris amet sint. Id mollit dolore adipisicing sunt dolor incididunt id non ipsum cupidatat. Sint proident do adipisicing veniam commodo adipisicing ad dolore anim dolor.

eu mollit duis

Eu ad nisi magna exercitation ullamco consequat minim ullamco tempor qui. Est proident ex fugiat do. Irure officia sint officia laborum veniam sint nisi. Laboris exercitation et commodo non cupidatat aliquip qui qui amet ut. Id aute consequat excepteur qui qui officia aute mollit.

Official source

https://mediaspace.epfl.ch/media/0_3xb1zavg

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.

Ontological neighbourhood

Mathematics

Algebra: Linear algebra

Information engineering

Machine learning: Topics in machine learning

Related lectures (32)