Learning for Efficient Retrieval of Structured Data with Noisy Queries

author: Charles Parker, Oregon State University
published: July 27, 2007,   recorded: July 2007,   views: 190

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.
  Bibliography

Description

Increasingly large collections of structured data necessitate the development of efficient, noise-tolerant retrieval tools. In this work, we consider this issue and describe an approach to learn a similarity function that is not only accurate, but that also increases the effectiveness of retrieval data structures. We present an algorithm that uses functional gradient boosting to maximize both retrieval accuracy and the retrieval efficiency of vantage point trees. We demonstrate the effectiveness of our approach on two datasets, including a moderately sized real-world dataset of folk music.

See Also:

Download slides icon Download slides: icml07_corvallis_parker_charles.pdf (554.8 KB)

Download slides icon Download slides: icml07_corvallis_parker_charles.ppt (359.5 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 !

Write your own review or comment:

make sure you have javascript enabled or clear this field: