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OpenDAS
dlib
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71b3605d
Commit
71b3605d
authored
Jul 07, 2013
by
Davis King
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clarified spec
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1c503219
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dlib/svm/structural_svm_problem_abstract.h
dlib/svm/structural_svm_problem_abstract.h
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dlib/svm/structural_svm_problem_abstract.h
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71b3605d
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@@ -34,14 +34,15 @@ namespace dlib
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@@ -34,14 +34,15 @@ namespace dlib
- This object will not be verbose
- This object will not be verbose
WHAT THIS OBJECT REPRESENTS
WHAT THIS OBJECT REPRESENTS
This object is a tool for solving the optimization problem associated
This object is a tool for solving the optimization problem associated
with
with
a structural support vector machine. A structural SVM is a supervised
a structural support vector machine. A structural SVM is a supervised
machine learning method for learning to predict complex outputs. This is
machine learning method for learning to predict complex outputs. This is
contrasted with a binary classifier which makes only simple yes/no predictions.
contrasted with a binary classifier which makes only simple yes/no
A structural SVM, on the other hand, can learn to predict outputs as complex
predictions. A structural SVM, on the other hand, can learn to predict
as entire parse trees. To do this, it learns a function F(x,y) which measures
complex outputs such as entire parse trees or DNA sequence alignments. To
how well a particular data sample x matches a label y. When used for prediction,
do this, it learns a function F(x,y) which measures how well a particular
the best label for a new x is given by the y which maximizes F(x,y).
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).
To use this object you inherit from it, provide implementations of its four
To use this object you inherit from it, provide implementations of its four
pure virtual functions, and then pass your object to the oca optimizer.
pure virtual functions, and then pass your object to the oca optimizer.
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