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钟尚武
dlib
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d134e2ec
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d134e2ec
authored
Jul 07, 2013
by
Davis King
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@@ -2624,11 +2624,12 @@ Davis E. King. <a href="http://jmlr.csail.mit.edu/papers/volume10/king09a/king09
for solving the optimization problem associated
with a structural support vector machine. A structural SVM is a supervised
machine learning method for learning to predict complex outputs. This is
contrasted with a binary classifier which makes only simple yes/no predictions.
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
how well a particular data sample x matches a label y. When used for prediction,
the best label for a new x is given by the y which maximizes F(x,y).
contrasted with a binary classifier which makes only simple yes/no
predictions. A structural SVM, on the other hand, can learn to predict
complex outputs such as entire parse trees or DNA sequence alignments. 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, the best label
for a new x is given by the y which maximizes F(x,y).
<p>
If you want to see example code that uses this object then take a look at
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