Predicting Structured Objects with Support Vector Machines
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.
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.