Structured support vector machine

<p>The structured support-vector machine is a <a href="/facts/Machine_learning/e0w0XJTu">machine learning</a> algorithm that generalizes the <a href="/facts/Support-vector_machine/XobxpdBG">Support-Vector Machine</a> (SVM) classifier.  Whereas the SVM classifier supports <a href="/facts/Binary_classification/MeYYAMwp">binary classification</a>, <a href="/facts/Multiclass_classification/aS7sV9EX">multiclass classification</a> and <a href="/facts/Regression_analysis/n6z5Tf7K">regression</a>, the structured SVM allows training of a classifier for general <a href="/facts/Structured_learning/hqe45TQD">structured output labels</a>.
</p><p>As an example, a sample instance might be a natural language sentence, and the output label is an annotated <a href="/facts/Parse_tree/FeBEtXcz">parse tree</a>.  Training a classifier consists of showing pairs of correct sample and output label pairs.  After training, the structured SVM model allows one to predict for new sample instances the corresponding output label; that is, given a natural language sentence, the classifier can produce the most likely parse tree.
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Structured support vector machine open-in-new

Structured support vector machine