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updated docs 

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Davis King 2011-03-23 23:44:08 +00:00
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@ -679,7 +679,7 @@ subject to the following constraint:
A structural SVM, on the other hand, can learn to predict outputs as complex A structural SVM, on the other hand, can learn to predict outputs as complex
as entire parse trees. To do this, it learns a function F(x,y) which measures as entire parse trees. To do this, it learns a function F(x,y) which measures
how well a particular data sample x matches a label y. When used for prediction, how well a particular data sample x matches a label y. When used for prediction,
the best label for an x is then given by the y which maximizes F(x,y). the best label for a new x is then given by the y which maximizes F(x,y).
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