Large Scale Sequence Labelling

author: Antoine Bordes, NEC Laboratories America, Inc.
published: Dec. 29, 2007,   recorded: December 2007,   views: 215

Slides

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

The general sequence labelling problem consists in processing an input sequence (xi) and producing an output sequence (yi) of discrete labels. Since the space of the possible output sequences is discrete, this can be viewed as a massive classification problem.
The notion of structured output prediction arises when one makes strong modelling assumption in order to learn the association with a reasonable number of examples. The conditional independence assumption states that a label it can be modelled as a function of the inputs (xt+i), i 2 I and the labels (yt+j), j 2 J for suitable choice of the sets I and J . The invariance assumption states that this function does not depend on t. The choice of sets I and J has a non trivial impact on the generalization performance and on the training and testing times.

See Also:

Download slides icon Download slides: eml07_bordes_lss_01.pdf (558.9┬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: