Machine Learning Reductions
author: John Langford,
Microsoft Research
published: Feb. 25, 2007, recorded: July 2006, views: 7698
published: Feb. 25, 2007, recorded: July 2006, views: 7698
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.
Description
There are several different classification problems commonly encountered in real world applications such as 'importance weighted classification', 'cost sensitive classification', 'reinforcement learning', 'regression' and others. Many of these problems can be related to each other by simple machines (reductions) that transform problems of one type into problems of another type. Finding a reduction from your problem to a more common problem allows the reuse of simple learning algorithms to solve relatively complex problems. It also induces an organization on learning problems — problems that can be easily reduced to each other are 'nearby' and problems which can not be so reduced are not close.
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: