Modern Nonparametric Statistics on Modern Big Data

author: Alexander Gray, School of Computational Science and Engineering, College of Computing, Georgia Institute of Technology
published: Jan. 16, 2013,   recorded: December 2012,   views: 6313
Categories

Related Open Educational Resources

Related content

Report a problem or upload files

If you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
Lecture popularity: You need to login to cast your vote.
  Bibliography

Description

Modern data is increasing very large in terms of both the number of objects and the number of dimensions. While in a statistical sense massive amounts of data make nonparametric methods entirely appropriate, their computational cost has made practitioners typically conclude that they are not possible in such scenarios. I will review a few common conceptual classes of nonparametric methods, including both classical and modern variants, then review recent algorithmic advances which can make nonparametric methods tractable

Link this page

Would you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !

Write your own review or comment:

make sure you have javascript enabled or clear this field: