Open AccessPredicting structured objects with support vector machines
Author(s) -
Thorsten Joachims,
Thomas Hofmann,
Yisong Yue,
Chun-Nam Yu
Publication year - 2009
Publication title -
communications of the acm
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.967
H-Index - 214
eISSN - 1557-7317
pISSN - 0001-0782
DOI - 10.1145/1592761.1592783
Subject(s) - support vector machine , computer science , generalization , artificial intelligence , machine learning , structured support vector machine , repertoire , data mining , mathematics , mathematical analysis , physics , acoustics
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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