Structured SVM

The structured support vector machine is a machine learning algorithm that generalizes the Support Vector Machine (SVM) classifier. Whereas the SVM classifier supports binary classification, multiclass classification and regression, the structured SVM allows training of a classifier for general structured output labels.

As an example, a sample instance might be a natural language sentence, and the output label is an annotated parse tree. 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.

For a set of ${\displaystyle \ell }$ training instances ${\displaystyle ({\boldsymbol {x}}_{n},y_{n})\in {\mathcal {X}}\times {\mathcal {Y}}}$, ${\displaystyle n=1,\dots ,\ell }$ from a sample space ${\displaystyle {\mathcal {X}}}$ and label space ${\displaystyle {\mathcal {Y}}}$, the structured SVM minimizes the following regularized risk function.

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