Predicting structured objects with support vector machines

Thorsten Joachims, Thomas Hofmann, Yisong Yue, Chun-Nam John Yu

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.