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Predicting Structured Objects with Support Vector Machines

By Thorsten Joachims, Thomas Hofmann, Yisong Yue, Chun-Nam Yu
Communications of the ACM, November 2009, Vol. 52 No. 11, Pages 97-104
10.1145/1592761.1592783
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green hyperplane between red and blue points — Some red and blue points in the hyperplane image are marked with yellow dots. In support vector machines, a predictive model is not parameterized in terms of the weights assigned to features but in terms of weights associated with each case.

Credit: Columbia Univ. Dept. of Statistics

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.

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