Generalization bounds

author: John Langford, Microsoft Research
published: Feb. 25, 2007,   recorded: March 2005,   views: 562
Categories

Slides

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.
  Delicious Bibliography

 Watch videos:   (click on thumbnail to launch)

Watch Part 1
Part 1 1:03:06
!NOW PLAYING
Watch Part 2
Part 2 39:30
!NOW PLAYING

Description

When a learning algorithm produces a classifier, a natural question to ask is "How well will it do in the future?" To make statements about the future given the past, some assumption must be made. If we make only an assumption that all examples are drawn independently and identically from some (unknown) distribution, we can answer the question. The answer to this question is directly applicable to classifier testing and confidence reporting. It also provides a simple general explanation of "overfitting", and influences algorithm design.

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: