Decision Tree and Instance-Based Learning for Label Ranking

author: Weiwei Cheng, Mathematik und Informatik, Philipps-Universität Marburg
published: Aug. 26, 2009,   recorded: June 2009,   views: 1423
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

Description

The label ranking problem consists of learning a model that maps instances to total orders over a finite set of predefined labels. This paper introduces new methods for label ranking that complement and improve upon existing approaches. More specifically, we propose extensions of two methods that have been used extensively for classification and regression so far, namely instance-based learning and decision tree induction. The unifying element of the two methods is a procedure for locally estimating predictive probability models for label rankings.

See Also:

Download slides icon Download slides: icml09_cheng_dtibllflr_01.pdf (930.6 KB)


Help icon Streaming Video Help

Link this page

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

Reviews and comments:

Comment1 小惠, September 15, 2009 at 3:46 a.m.:

I really like your lecture even through I don't know exactly the professional knowledge of your speech.If I'm the student of your class,I'll really enjoy this perfect lecture.
Congratulations!

Write your own review or comment:

make sure you have javascript enabled or clear this field: