Learning with structured data - structured outputs

author: Patrick Gallinari, Université Pierre et Marie Curie - Paris 6
published: Nov. 26, 2007,   recorded: September 2007,   views: 380


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


We focus on the prediction of structured outputs. A classical example is sequence labeling with applications in speech, vision, natural language or biology. Beyond sequences, the prediction of structured data, like trees, lattices or graphs also occurs in many domains. Structured prediction is usually considered as an extension of multi-class classification. It is considered as a challenging problem since the size of the output space increases drastically with the number of potential dependencies between output variables. Several methods have been recently proposed in the ML community in order to overcome this complexity and the domain is still largely open. We will provide a review of these methods and discuss there potential and limitations. These different ideas will be illustrated with Natural language processing and text mining applications.

See Also:

Download slides icon Download slides: mmdss07_gallinari_lsd_01.ppt (2.5¬†MB)

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: