Lecture

Kernel Regression: Weighted Average and Feature Maps

Description

This lecture covers the concept of kernel regression, focusing on the calculation of weighted averages and the derivation of feature maps using radial basis functions. The instructor explains the process step by step, emphasizing the importance of feature mapping in making data linearly separable.

Instructor

quis voluptate non

Culpa exercitation sit nisi cillum velit incididunt pariatur. Id ut amet esse eiusmod consectetur voluptate deserunt. Sit aliquip nostrud aliqua tempor deserunt cillum quis proident enim nulla ullamco eu nisi. Eu ipsum est cupidatat nisi sint laboris irure dolore nisi eu.

Official source

https://mediaspace.epfl.ch/media/0_cdxsfi33

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

Statistics

Data analysis: Regression analysis

Statistical inference: Mathematical statistics, Non-parametric statistics

Information engineering

Machine learning: Topics in machine learning, Kernel methods

Mathematics

Algebra: Linear algebra

Related lectures (266)

Graph Chatbot

Chat with Graph Search

Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.

DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.