Published November 2009 | Version public
Journal Article

Predicting Structured Objects with Support Vector Machines

Abstract

Machine Learning today offers a broad repertoire of methods for classification and regression. But what if we need to predict complex objects like trees, orderings, or alignments? Such problems arise naturally in natural language processing, search engines, and bioinformatics. The following explores a generalization of Support Vector Machines (SVMs) for such complex prediction problems.

Additional Information

Copyright © 2009 ACM. This work was supported in part through NSF Awards IIS- 0412894 and IIS-0713483, NIH Grants IS10RR020889 and GM67823, a gift from Yahoo!, and by Google.

Additional details

Identifiers

Eprint ID
49360
DOI
10.1145/1592761.1592783
Resolver ID
CaltechAUTHORS:20140908-153800463

Related works

Funding

NSF
IIS-0412894
NSF
IIS-0713483
NIH
IS10RR020889
NIH
GM67823
Yahoo!
Google

Dates

Created
2014-09-08
Created from EPrint's datestamp field
Updated
2021-11-10
Created from EPrint's last_modified field