Large-Margin Thresholded Ensembles for Ordinal Regression

author: Hsuan-Tien Lin, Department of Computer Science and Information Engineering, National Taiwan University
published: Feb. 25, 2007,   recorded: July 2006,   views: 336
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

We propose a thresholded ensemble model for ordinal regression problems. The model consists of a weighted ensemble of confidence functions and an ordered vector of thresholds. Using such a model, we could theoretically and algorithmically reduce ordinal regression problems to binary classification problems in the area of ensemble learning. Based on the reduction, we derive novel large-margin bounds of common error functions, such as the classification error and the absolute error. In addition, we also design two novel boosting approaches for constructing thresholded ensembles. Both our approaches have comparable performance to the state-of-the-art algorithms, but enjoy the benefit of faster training. Experimental results on benchmark datasets demonstrate the usefulness of our boosting approaches.

See Also:

Download slides icon Download slides: mlss06tw_lin_lmteo.pdf (414.3┬áKB)


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 !

Write your own review or comment:

make sure you have javascript enabled or clear this field: